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Journal articles on the topic 'Global and regional models'

Author: Grafiati

Published: 10 March 2023

Last updated: 11 March 2023

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1

Ustun, A., and R. A. Abbak. "On global and regional spectral evaluation of global geopotential models." Journal of Geophysics and Engineering 7, no. 4 (September 10, 2010): 369–79. http://dx.doi.org/10.1088/1742-2132/7/4/003.

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2

CRIHFIELD, JOHN B., and HARRISON S. CAMPBELL. "Evaluating Alternative Regional Planning Models." Growth and Change 22, no. 2 (April 1991): 1–16. http://dx.doi.org/10.1111/j.1468-2257.1991.tb00544.x.

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3

Straus, Julian, Jabir Ali Ouassou, Ove Wolfgang, and Gunhild Allard Reigstad. "Introducing global learning in regional energy system models." Energy Strategy Reviews 38 (November 2021): 100763. http://dx.doi.org/10.1016/j.esr.2021.100763.

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4

Bertotti, L., L. Cavaleri, A. Soret, and R. Tolosana-Delgado. "Performance of global and regional nested meteorological models." Continental Shelf Research 87 (September 2014): 17–27. http://dx.doi.org/10.1016/j.csr.2013.12.013.

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5

GRIMES, DONALD R., GEORGE A. FULTON, and MARC A. BONARDELLI. "Evaluating Alternative Regional Planning Models: Comment." Growth and Change 23, no. 4 (October 1992): 516–20. http://dx.doi.org/10.1111/j.1468-2257.1992.tb00947.x.

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CRIHFIELD, JOHN B., and HARRISON S. CAMPBELL. "Evaluating Alternative Regional Planning Models: Reply." Growth and Change 23, no. 4 (October 1992): 521–30. http://dx.doi.org/10.1111/j.1468-2257.1992.tb00948.x.

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7

Basciftci, Fuat, Cevat Inal, Omer Yildirim, and Sercan Bulbul. "Determining regional ionospheric model and comparing with global models." Geodetski vestnik 61, no. 03 (2017): 427–40. http://dx.doi.org/10.15292//geodetski-vestnik.2017.03.427-440.

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8

Pierce, D. W., T. P. Barnett, B. D. Santer, and P. J. Gleckler. "Selecting global climate models for regional climate change studies." Proceedings of the National Academy of Sciences 106, no. 21 (May 13, 2009): 8441–46. http://dx.doi.org/10.1073/pnas.0900094106.

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9

McKone, Thomas E., and Matthew MacLeod. "TRACKINGMULTIPLEPATHWAYS OFHUMANEXPOSURE TOPERSISTENTMULTIMEDIAPOLLUTANTS: Regional, Continental, and Global-Scale Models." Annual Review of Environment and Resources 28, no. 1 (November 2003): 463–92. http://dx.doi.org/10.1146/annurev.energy.28.050302.105623.

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10

Joyce, Linda A., Thomas W. Hoekstra, and Ralph J. Alig. "Regional multiresource models in a national framework." Environmental Management 10, no. 6 (November 1986): 761–71. http://dx.doi.org/10.1007/bf01867729.

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11

Pitman, A. J., A. Henderson-Sellers, and Z.-L. Yang. "Sensitivity of regional climates to localized precipitation in global models." Nature 346, no. 6286 (August 1990): 734–37. http://dx.doi.org/10.1038/346734a0.

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12

Mosayebzadeh, Mahdi, Alireza A. Ardalan, and Roohollah Karimi. "Regional improvement of global geopotential models using GPS/Leveling data." Studia Geophysica et Geodaetica 63, no. 2 (April 2019): 169–90. http://dx.doi.org/10.1007/s11200-017-1084-9.

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13

Liston, Glen E. "Representing Subgrid Snow Cover Heterogeneities in Regional and Global Models." Journal of Climate 17, no. 6 (March 2004): 1381–97. http://dx.doi.org/10.1175/1520-0442(2004)017<1381:rsschi>2.0.co;2.

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14

Caldwell, Peter. "California Wintertime Precipitation Bias in Regional and Global Climate Models." Journal of Applied Meteorology and Climatology 49, no. 10 (October 1, 2010): 2147–58. http://dx.doi.org/10.1175/2010jamc2388.1.

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Abstract In this paper, wintertime precipitation from a variety of observational datasets, regional climate models (RCMs), and general circulation models (GCMs) is averaged over the state of California and compared. Several averaging methodologies are considered and all are found to give similar values when the model grid spacing is less than 3°. This suggests that California is a reasonable size for regional intercomparisons using modern GCMs. Results show that reanalysis-forced RCMs tend to significantly overpredict California precipitation. This appears to be due mainly to the overprediction of extreme events; RCM precipitation frequency is generally underpredicted. Overprediction is also reflected in wintertime precipitation variability, which tends to be too high for RCMs on both daily and interannual scales. Wintertime precipitation in most (but not all) GCMs is underestimated. This is in contrast to previous studies based on global blended gauge–satellite observations, which are shown here to underestimate precipitation relative to higher-resolution gauge-only datasets. Several GCMs provide reasonable daily precipitation distributions, a trait that does not seem to be tied to model resolution. The GCM daily and interannual variabilities are generally underpredicted.

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15

Bechtold, P., E. Bazile, F. Guichard, P. Mascart, and E. Richard. "A mass-flux convection scheme for regional and global models." Quarterly Journal of the Royal Meteorological Society 127, no. 573 (April 2001): 869–86. http://dx.doi.org/10.1002/qj.49712757309.

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16

DE, U. S. "Climate change impact : Regional scenario." MAUSAM 52, no. 1 (December 29, 2021): 201–12. http://dx.doi.org/10.54302/mausam.v52i1.1688.

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Climate change and global warming are going to be the major issues for the 21st century. Their impacts on agriculture, water availability and other natural resources are of serious concern. The paper briefly summarizes the existing information on global warming, past climatic anomalies and occurrence of extreme events vis-a-vis their impact on south Asia in general and Indian in particular. Use of GCM models in conjunction with crop simulation models for impact assessment in agriculture are briefly touched upon. The impact on hydrosphere in terms of water availability and on the forests in India are also discussed. A major shift in our policy makers paradigm is needed to make development sustainable in the face of climate change, global warming and sea level rise.

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17

Wilcke, Renate Anna Irma, Thomas Mendlik, and Andreas Gobiet. "Multi-variable error correction of regional climate models." Climatic Change 120, no. 4 (August 14, 2013): 871–87. http://dx.doi.org/10.1007/s10584-013-0845-x.

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18

Jámbor, Attila, Péter Gál, and Áron Török. "Determinants of regional trade agreements: Global evidence based on gravity models." Journal of International Studies 13, no. 1 (March 2020): 44–57. http://dx.doi.org/10.14254/2071-8330.2020/13-1/3.

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19

Jacques, Peter. "Downscaling climate models and environmental policy: From global to regional politics." Journal of Environmental Planning and Management 49, no. 2 (March 2006): 301–7. http://dx.doi.org/10.1080/09640560500508205.

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20

Berbery, Ernesto Hugo, Kenneth E. Mitchell, Stanley Benjamin, Tatiana Smirnova, Harold Ritchie, Richard Hogue, and Ekaterina Radeva. "Assessment of land-surface energy budgets from regional and global models." Journal of Geophysical Research: Atmospheres 104, no. D16 (August 1, 1999): 19329–48. http://dx.doi.org/10.1029/1999jd900128.

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21

Klees, R., X. Liu, T. Wittwer, B. C. Gunter, E. A. Revtova, R. Tenzer, P. Ditmar, H. C. Winsemius, and H. H. G. Savenije. "A Comparison of Global and Regional GRACE Models for Land Hydrology." Surveys in Geophysics 29, no. 4-5 (October 2008): 335–59. http://dx.doi.org/10.1007/s10712-008-9049-8.

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22

Jenkins, Gregory S., Andre Kamga, Adamou Garba, Arona Diedhiou, Vernon Morris, and Everette Joseph. "Investigating the West African Climate System Using Global/Regional Climate Models." Bulletin of the American Meteorological Society 83, no. 4 (April 2002): 583–95. http://dx.doi.org/10.1175/1520-0477(2002)083<0583:itwacs>2.3.co;2.

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23

Dolff-Bonekämper, Gabi, and Jonathan Blower. "National—Regional—Global? Old and New Models of Societal Heritage Constructions." Art in Translation 4, no. 3 (September 2012): 275–86. http://dx.doi.org/10.2752/175613112x13376070683234.

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24

Zhang, Yongqiang, Hongxing Zheng, Francis H. S. Chiew, Jorge Peña Arancibia, and Xinyao Zhou. "Evaluating Regional and Global Hydrological Models against Streamflow and Evapotranspiration Measurements." Journal of Hydrometeorology 17, no. 3 (March 1, 2016): 995–1010. http://dx.doi.org/10.1175/jhm-d-15-0107.1.

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Abstract Land surface and global hydrological models are often used to characterize global water and energy fluxes and stores and to model their future trajectories. This study evaluates estimates of streamflow and evapotranspiration (ET) obtained with a priori parameterization from a land surface model [CSIRO Atmosphere Biosphere Land Exchange (CABLE)] and a global hydrological model (H08) against a global dataset of streamflow from 644 largely unregulated catchments and ET from 98 flux towers and benchmarks their performance against two lumped conceptual daily rainfall–runoff models [modèle du Génie Rural à 4 paramètres Journalier (GR4J) and a simplified version of the HYDROLOG model (SIMHYD)]. The results show that all four models perform poorly in simulating the monthly and annual runoff values, with the rainfall–runoff models outperforming both CABLE and H08. The model biases in runoff are generally reflected as a complementary opposite bias in ET. All models can generally reproduce the observed seasonal and interannual runoff variability. The correlations between the modeled and observed runoff time series are reasonable, with the rainfall–runoff models performing slightly better than CABLE and H08 at the monthly time scale and all four models performing similarly at the annual time scale. The results suggest that while the land surface and global hydrological models cannot adequately simulate the actual runoff time series and long-term average volumes, they can reasonably simulate the monthly and interannual runoff variability and trends and can therefore be reliably used for broadscale or comparative regional and global water and energy balance assessments and simulations of future trajectories. They can be improved through validating the models or calibrating some of the more sensitive and less physically based parameters.

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25

Perkins, Sarah E., Andy J. Pitman, Neil J. Holbrook, and John McAneney. "Ability of global climate models at a regional scale over Australia." International Journal of Global Environmental Issues 7, no. 4 (2007): 275. http://dx.doi.org/10.1504/ijgenvi.2007.016108.

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26

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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27

Siqueira, Vinícius A., Rodrigo C. D. Paiva, Ayan S. Fleischmann, Fernando M. Fan, Anderson L. Ruhoff, Paulo R. M. Pontes, Adrien Paris, Stéphane Calmant, and Walter Collischonn. "Toward continental hydrologic–hydrodynamic modeling in South America." Hydrology and Earth System Sciences 22, no. 9 (September 18, 2018): 4815–42. http://dx.doi.org/10.5194/hess-22-4815-2018.

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Abstract. Providing reliable estimates of streamflow and hydrological fluxes is a major challenge for water resources management over national and transnational basins in South America. Global hydrological models and land surface models are a possible solution to simulate the terrestrial water cycle at the continental scale, but issues about parameterization and limitations in representing lowland river systems can place constraints on these models to meet local needs. In an attempt to overcome such limitations, we extended a regional, fully coupled hydrologic–hydrodynamic model (MGB; Modelo hidrológico de Grandes Bacias) to the continental domain of South America and assessed its performance using daily river discharge, water levels from independent sources (in situ, satellite altimetry), estimates of terrestrial water storage (TWS) and evapotranspiration (ET) from remote sensing and other available global datasets. In addition, river discharge was compared with outputs from global models acquired through the eartH2Observe project (HTESSEL/CaMa-Flood, LISFLOOD and WaterGAP3), providing the first cross-scale assessment (regional/continental × global models) that makes use of spatially distributed, daily discharge data. A satisfactory representation of discharge and water levels was obtained (Nash–Sutcliffe efficiency, NSE > 0.6 in 55 % of the cases) and the continental model was able to capture patterns of seasonality and magnitude of TWS and ET, especially over the largest basins of South America. After the comparison with global models, we found that it is possible to obtain considerable improvement on daily river discharge, even by using current global forcing data, just by combining parameterization and better routing physics based on regional experience. Issues about the potential sources of errors related to both global- and continental-scale modeling are discussed, as well as future directions for improving large-scale model applications in this continent. We hope that our study provides important insights to reduce the gap between global and regional hydrological modeling communities.

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28

French, Joshua P., Seth McGinnis, and Armin Schwartzman. "Assessing NARCCAP climate model effects using spatial confidence regions." Advances in Statistical Climatology, Meteorology and Oceanography 3, no. 2 (July 14, 2017): 67–92. http://dx.doi.org/10.5194/ascmo-3-67-2017.

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Abstract. We assess similarities and differences between model effects for the North American Regional Climate Change Assessment Program (NARCCAP) climate models using varying classes of linear regression models. Specifically, we consider how the average temperature effect differs for the various global and regional climate model combinations, including assessment of possible interaction between the effects of global and regional climate models. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We also show conclusively that results from pointwise inference are misleading, and that accounting for multiple comparisons is important for making proper inference.

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29

Scinocca, J. F., V. V. Kharin, Y. Jiao, M. W. Qian, M. Lazare, L. Solheim, G. M. Flato, S. Biner, M. Desgagne, and B. Dugas. "Coordinated Global and Regional Climate Modeling*." Journal of Climate 29, no. 1 (December 22, 2015): 17–35. http://dx.doi.org/10.1175/jcli-d-15-0161.1.

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Abstract A new approach of coordinated global and regional climate modeling is presented. It is applied to the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) and its parent global climate model CanESM2. CanRCM4 was developed specifically to downscale climate predictions and climate projections made by its parent global model. The close association of a regional climate model (RCM) with a parent global climate model (GCM) offers novel avenues of model development and application that are not typically available to independent regional climate modeling centers. For example, when CanRCM4 is driven by its parent model, driving information for all of its prognostic variables is available (including aerosols and chemical species), significantly improving the quality of their simulation. Additionally, CanRCM4 can be driven by its parent model for all downscaling applications by employing a spectral nudging procedure in CanESM2 designed to constrain its evolution to follow any large-scale driving data. Coordination offers benefit to the development of physical parameterizations and provides an objective means to evaluate the scalability of such parameterizations across a range of spatial resolutions. Finally, coordinating regional and global modeling efforts helps to highlight the importance of assessing RCMs’ value added relative to their driving global models. As a first step in this direction, a framework for identifying appreciable differences in RCM versus GCM climate change results is proposed and applied to CanRCM4 and CanESM2.

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Anh, V., Z. G. Yu, and J. A. Wanliss. "Analysis of global geomagnetic variability." Nonlinear Processes in Geophysics 14, no. 6 (November 23, 2007): 701–8. http://dx.doi.org/10.5194/npg-14-701-2007.

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Abstract. The orthogonal field components from global INTERMAGNET magnetometer stations are studied via multifractal detrended fluctuation analysis to determine whether there are clear and consistent regional patterns in the behavior of the fluctuations. There are three distinct scaling regimes in the qth-order fluctuation function for each of the 24 stations studied covering Southwest North America, Northeast North America, Central Europe, Northern Europe, Australasia and Asia. There is a consistent break point at time scale around 23 h for all stations. The scaling exponents of the second-order fluctuation functions reflect the regional character of the stations, and can be used for station classification, and for possible regional models.

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Rummukainen, M., J. Räisänen, D. Bjørge, J. H. Christensen, O. B. Christensen, T. Iversen, K. Jylhä, H. Ólafsson, and H. Tuomenvirta. "Regional Climate Scenarios for use in Nordic Water Resources Studies." Hydrology Research 34, no. 5 (October 1, 2003): 399–412. http://dx.doi.org/10.2166/nh.2003.0014.

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According to global climate projections, a substantial global climate change will occur during the next decades, under the assumption of continuous anthropogenic climate forcing. Global models, although fundamental in simulating the response of the climate system to anthropogenic forcing are typically geographically too coarse to well represent many regional or local features. In the Nordic region, climate studies are conducted in each of the Nordic countries to prepare regional climate projections with more detail than in global ones. Results so far indicate larger temperature changes in the Nordic region than in the global mean, regional increases and decreases in net precipitation, longer growing season, shorter snow season etc. These in turn affect runoff, snowpack, groundwater, soil frost and moisture, and thus hydropower production potential, flooding risks etc. Regional climate models do not yet fully incorporate hydrology. Water resources studies are carried out off-line using hydrological models. This requires archived meteorological output from climate models. This paper discusses Nordic regional climate scenarios for use in regional water resources studies. Potential end-users of water resources scenarios are the hydropower industry, dam safety instances and planners of other lasting infrastructure exposed to precipitation, river flows and flooding.

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32

Leroy, Stephen S., and James G. Anderson. "Optimal Detection of Regional Trends Using Global Data." Journal of Climate 23, no. 16 (August 15, 2010): 4438–46. http://dx.doi.org/10.1175/2010jcli3550.1.

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Abstract A complete accounting of model uncertainty in the optimal detection of climate signals requires normalization of the signals produced by climate models; however, there is not yet a well-defined rule for the normalization. This study seeks to discover such a rule. The authors find that, to arrive at the equations of optimal detection from a general application of Bayesian statistics to the problem of climate change, it is necessary to assume that 1) the prior probability density function (PDF) for climate change is separable into independent PDFs for sensitivity and the signals’ spatiotemporal patterns; 2) postfit residuals are due to internal variability and are normally distributed; 3) the prior PDF for sensitivity is uninformative; and 4) a continuum of climate models used to estimate model uncertainty gives a normally distributed PDF for the spatiotemporal patterns for the climate signals. This study also finds that the rule for normalization of the signals’ patterns is a simple division of model-simulated climate change in any observable quantity or set of quantities by a change in a single quantity of interest such as regionally averaged temperature or precipitation. With this normalization, optimal detection yields the most probable estimates of the underlying changes in the region of interest due to external forcings. Data outside the region of interest add information that effectively suppresses the interannual fluctuations associated with internal climate variability.

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33

Camargo, Suzana J. "Global and Regional Aspects of Tropical Cyclone Activity in the CMIP5 Models." Journal of Climate 26, no. 24 (December 2, 2013): 9880–902. http://dx.doi.org/10.1175/jcli-d-12-00549.1.

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Abstract Tropical cyclone (TC) activity is analyzed in 14 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). The global TC activity in the historical runs is compared with observations. The simulation of TC activity in the CMIP5 models is not as good as in higher-resolution simulations. The CMIP5 global TC frequency is much lower than observed, and there is significant deficiency in the geographical patterns of TC tracks and formation. Although all of the models underestimate the global frequency of TCs, the models present a wide range of global TC frequency. The models with the highest horizontal resolution have the highest level of global TC activity, though resolution is not the only factor that determines model TC activity. A cold SST bias could potentially contribute to the low number of TCs in the models. The models show no consensus regarding the difference of TC activity in two warming scenarios [representative concentration pathway 4.5 (RCP4.5) and RCP8.5] and the historical simulation. The author examined in more detail North Atlantic and eastern North Pacific TC activity in a subset of models and found no robust changes across models in TC frequency. Therefore, there is no robust signal across the CMIP5 models in global and regional TC changes in activity for future scenarios. The future changes in various large-scale environmental fields associated with TC activity were also examined globally: genesis potential index, potential intensity, vertical wind shear, and sea level pressure. The multimodel mean changes of these variables in the CMIP5 models are consistent with the changes obtained in the CMIP3 models.

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Karypidou, Maria Chara, Eleni Katragkou, and Stefan Pieter Sobolowski. "Precipitation over southern Africa: is there consensus among global climate models (GCMs), regional climate models (RCMs) and observational data?" Geoscientific Model Development 15, no. 8 (April 22, 2022): 3387–404. http://dx.doi.org/10.5194/gmd-15-3387-2022.

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Abstract. The region of southern Africa (SAF) is highly vulnerable to the impacts of climate change and is projected to experience severe precipitation shortages in the coming decades. Ensuring that our modeling tools are fit for the purpose of assessing these changes is critical. In this work we compare a range of satellite products along with gauge-based datasets. Additionally, we investigate the behavior of regional climate simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX) – Africa domain, along with simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6). We identify considerable variability in the standard deviation of precipitation between satellite products that merge with rain gauges and satellite products that do not, during the rainy season (October–March), indicating high observational uncertainty for specific regions over SAF. Good agreement both in spatial pattern and the strength of the calculated trends is found between satellite and gauge-based products, however. Both CORDEX-Africa and CMIP ensembles underestimate the observed trends during the analysis period. The CMIP6 ensemble displayed persistent drying trends, in direct contrast to the observations. The regional ensembles exhibited improved performance compared to their forcing (CMIP5), when the annual cycle and the extreme precipitation indices were examined, confirming the added value of the higher-resolution regional climate simulations. The CMIP6 ensemble displayed a similar behavior to CMIP5, but reducing slightly the ensemble spread. However, we show that reproduction of some key SAF phenomena, like the Angola Low (which exerts a strong influence on regional precipitation), still poses a challenge for the global and regional models. This is likely a result of the complex climatic processes that take place. Improvements in observational networks (both in situ and satellite) as well as continued advancements in high-resolution modeling will be critical, in order to develop a robust assessment of climate change for southern Africa.

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35

Staten, Paul W., Kevin M. Grise, Sean M. Davis, Kristopher B. Karnauskas, Darryn W. Waugh, Amanda C. Maycock, Qiang Fu, et al. "Tropical Widening: From Global Variations to Regional Impacts." Bulletin of the American Meteorological Society 101, no. 6 (June 1, 2020): E897—E904. http://dx.doi.org/10.1175/bams-d-19-0047.1.

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Abstract Over the past 15 years, numerous studies have suggested that the sinking branches of Earth’s Hadley circulation and the associated subtropical dry zones have shifted poleward over the late twentieth century and early twenty-first century. Early estimates of this tropical widening from satellite observations and reanalyses varied from 0.25° to 3° latitude per decade, while estimates from global climate models show widening at the lower end of the observed range. In 2016, two working groups, the U.S. Climate Variability and Predictability (CLIVAR) working group on the Changing Width of the Tropical Belt and the International Space Science Institute (ISSI) Tropical Width Diagnostics Intercomparison Project, were formed to synthesize current understanding of the magnitude, causes, and impacts of the recent tropical widening evident in observations. These working groups concluded that the large rates of observed tropical widening noted by earlier studies resulted from their use of metrics that poorly capture changes in the Hadley circulation, or from the use of reanalyses that contained spurious trends. Accounting for these issues reduces the range of observed expansion rates to 0.25°–0.5° latitude decade‒1—within the range from model simulations. Models indicate that most of the recent Northern Hemisphere tropical widening is consistent with natural variability, whereas increasing greenhouse gases and decreasing stratospheric ozone likely played an important role in Southern Hemisphere widening. Whatever the cause or rate of expansion, understanding the regional impacts of tropical widening requires additional work, as different forcings can produce different regional patterns of widening.

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36

Dolman, A. J., J. Noilhan, P. Durand, C. Sarrat, A. Brut, B. Piguet, A. Butet, et al. "The CarboEurope Regional Experiment Strategy." Bulletin of the American Meteorological Society 87, no. 10 (October 1, 2006): 1367–80. http://dx.doi.org/10.1175/bams-87-10-1367.

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The Second Global Soil Wetness Project (GSWP-2) is an initiative to compare and evaluate 10-year simulations by a broad range of land surface models under controlled conditions. A major product of GSWP-2 is the first global gridded multimodel analysis of land surface state variables and fluxes for use by meteorologists, hydrologists, engineers, biogeochemists, agronomists, botanists, ecologists, geographers, climatologists, and educators. Simulations by 13 land models from five nations have gone into production of the analysis. The models are driven by forcing data derived from a combination of gridded atmospheric reanalyses and observations. The resulting analysis consists of multimodel means and standard deviations on the monthly time scale, including profiles of soil moisture and temperature at six levels, as well as daily and climatological (mean annual cycle) fields for over 50 land surface variables. The monthly standard deviations provide a measure of model agreement that may be used as a quality metric. An overview of key characteristics of the analysis is presented here, along with information on obtaining the data.

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37

Feser, Frauke, Burkhardt Rockel, Hans von Storch, Jörg Winterfeldt, and Matthias Zahn. "Regional Climate Models Add Value to Global Model Data: A Review and Selected Examples." Bulletin of the American Meteorological Society 92, no. 9 (September 1, 2011): 1181–92. http://dx.doi.org/10.1175/2011bams3061.1.

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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.

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Aamaas, Borgar, Terje K. Berntsen, Jan S. Fuglestvedt, Keith P. Shine, and Nicolas Bellouin. "Regional emission metrics for short-lived climate forcers from multiple models." Atmospheric Chemistry and Physics 16, no. 11 (June 15, 2016): 7451–68. http://dx.doi.org/10.5194/acp-16-7451-2016.

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Abstract. For short-lived climate forcers (SLCFs), the impact of emissions depends on where and when the emissions take place. Comprehensive new calculations of various emission metrics for SLCFs are presented based on radiative forcing (RF) values calculated in four different (chemical-transport or coupled chemistry–climate) models. We distinguish between emissions during summer (May–October) and winter (November–April) for emissions in Europe and East Asia, as well as from the global shipping sector and global emissions. The species included in this study are aerosols and aerosol precursors (BC, OC, SO2, NH3), as well as ozone precursors (NOx, CO, VOCs), which also influence aerosols to a lesser degree. Emission metrics for global climate responses of these emissions, as well as for CH4, have been calculated using global warming potential (GWP) and global temperature change potential (GTP), based on dedicated RF simulations by four global models. The emission metrics include indirect cloud effects of aerosols and the semi-direct forcing for BC. In addition to the standard emission metrics for pulse and sustained emissions, we have also calculated a new emission metric designed for an emission profile consisting of a ramping period of 15 years followed by sustained emissions, which is more appropriate for a gradual implementation of mitigation policies.For the aerosols, the emission metric values are larger in magnitude for emissions in Europe than East Asia and for summer than winter. A variation is also observed for the ozone precursors, with largest values for emissions in East Asia and winter for CO and in Europe and summer for VOCs. In general, the variations between the emission metrics derived from different models are larger than the variations between regions and seasons, but the regional and seasonal variations for the best estimate also hold for most of the models individually. Further, the estimated climate impact of an illustrative mitigation policy package is robust even when accounting for the fact that the magnitude of emission metrics for different species in a given model is correlated. For the ramping emission metrics, the values are generally larger than for pulse or sustained emissions, which holds for all SLCFs. For SLCFs mitigation policies, the dependency of metric values on the region and season of emission should be considered.

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Skogen, Morten D., Solfrid S. Hjøllo, Anne Britt Sandø, and Jerry Tjiputra. "Future ecosystem changes in the Northeast Atlantic: a comparison between a global and a regional model system." ICES Journal of Marine Science 75, no. 7 (July 19, 2018): 2355–69. http://dx.doi.org/10.1093/icesjms/fsy088.

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Abstract The biogeochemistry from a global climate model (Norwegian Earth System Model) has been compared with results from a regional model (NORWECOM.E2E), where the regional model is forced by downscaled physics from the global model. The study should both be regarded as a direct comparison between a regional and its driving global model to investigate at what extent a global climate model can be used for regional studies, and a study of the future climate change in the Nordic and Barents Seas. The study concludes that the global and regional model compare well on trends, but many details are lost when a coarse resolution global model is used to assess climate impact on regional scale. The main difference between the two models is the timing of the spring bloom, and a non-exhaustive nutrient consumption in the global model in summer. The global model has a cold (in summer) and saline bias compared with climatology. This is both due to poorly resolved physical processes and oversimplified ecosystem parameterization. Through the downscaling the regional model is to some extent able to alleviate the bias in the physical fields, and the timing of the spring bloom is close to observations. The summer nutrient minimum is one month early. There is no trend in future primary production in any of the models, and the trends in modelled pH and ΩAr are also the same in both models. The largest discrepancy in the future projection is in the development of the CO2 uptake, where the regional suggests a slightly reduced uptake in the future.

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Ionescu, Romeo-Victor, Monica Laura Zlati, and Valentin Marian Antohi. "Global Challenges vs. the Need for Regional Performance Models under the Present Pandemic Crisis." International Journal of Environmental Research and Public Health 18, no. 19 (September 29, 2021): 10254. http://dx.doi.org/10.3390/ijerph181910254.

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The present study uses the analysis of the EU’s regional performance structure based on clusters to test the versatility of the regional administrative capacity in relation to three disruptive global phenomena: the economic crisis, the coronavirus epidemic and the phenomenon of refugee migration to Europe. We defined a regional performance model based on maintaining sustainability indicators in the 240 EU regions. The objectives of the study are aimed primarily at a structured assessment of regional administrative capacity in the initial version, based on statistical indicators, and in the current version, after the outbreak of the pandemic, based on quantifying the impact of the disturbing factors. Secondly, the objectives of the study are to evaluate the reaction of the administrative units according to their ability to respond to the economic problems in the region, in the sense of improving the performance of the regional economies. The methods used in this paper will be empirical (the study of the specialized literature), analytical and will contain econometric modelling and statistical processing of the data. The results of the study will allow the identification of the necessary traits to train a leader in regional performance, traits that will be useful to European decision makers in adjusting the EU regional policy. Moreover, the need to redefine the EU in terms of performance will be substantiated once again. The study is current and is based on the latest Eurostat information, pertinent tables and diagrams.

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Zhao, Li, David C. Alsop, John A. Detre, and Weiying Dai. "Global fluctuations of cerebral blood flow indicate a global brain network independent of systemic factors." Journal of Cerebral Blood Flow & Metabolism 39, no. 2 (August 17, 2017): 302–12. http://dx.doi.org/10.1177/0271678x17726625.

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Global synchronization across specialized brain networks is a common feature of network models and in-vivo electrical measurements. Although the imaging of specialized brain networks with blood oxygenation sensitive resting state functional magnetic resonance imaging (rsfMRI) has enabled detailed study of regional networks, the study of globally correlated fluctuations with rsfMRI is confounded by spurious contributions to the global signal from systemic physiologic factors and other noise sources. Here we use an alternative rsfMRI method, arterial spin labeled perfusion MRI, to characterize global correlations and their relationship to correlations and anti-correlations between regional networks. Global fluctuations that cannot be explained by systemic factors dominate the fluctuations in cerebral blood flow. Power spectra of these fluctuations are band limited to below 0.05 Hz, similar to prior measurements of regional network fluctuations in the brain. Removal of these global fluctuations prior to measurement of regional networks reduces all regional network fluctuation amplitudes to below the global fluctuation amplitude and changes the strength and sign of inter network correlations. Our findings support large amplitude, globally synchronized activity across networks that require a reassessment of regional network amplitude and correlation measures.

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Ramirez-Villegas, Julian, Andrew J. Challinor, Philip K. Thornton, and Andy Jarvis. "Implications of regional improvement in global climate models for agricultural impact research." Environmental Research Letters 8, no. 2 (May 3, 2013): 024018. http://dx.doi.org/10.1088/1748-9326/8/2/024018.

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Meschede, Matthias, and Barbara Romanowicz. "Lateral heterogeneity scales in regional and global upper mantle shear velocity models." Geophysical Journal International 200, no. 2 (December 30, 2014): 1078–95. http://dx.doi.org/10.1093/gji/ggu424.

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Hermann, A. J., D. B. Haidvogel, E. L. Dobbins, and P. J. Stabeno. "Coupling global and regional circulation models in the coastal Gulf of Alaska." Progress in Oceanography 53, no. 2-4 (January 2002): 335–67. http://dx.doi.org/10.1016/s0079-6611(02)00036-8.

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Thomas, G., A. Henderson‐Sellers, and A. J. Pitman. "Regional‐scale surface hydrologic simulations from global climate models: A case study." Atmosphere-Ocean 29, no. 3 (September 1991): 420–36. http://dx.doi.org/10.1080/07055900.1991.9649411.

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Dawson, Nicholas, Patrick Broxton, Xubin Zeng, Michael Leuthold, Michael Barlage, and Pat Holbrook. "An Evaluation of Snow Initializations in NCEP Global and Regional Forecasting Models." Journal of Hydrometeorology 17, no. 6 (June 1, 2016): 1885–901. http://dx.doi.org/10.1175/jhm-d-15-0227.1.

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Abstract Snow plays a major role in land–atmosphere interactions, but strong spatial heterogeneity in snow depth (SD) and snow water equivalent (SWE) makes it challenging to evaluate gridded snow quantities using in situ measurements. First, a new method is developed to upscale point measurements into gridded datasets that is superior to other tested methods. It is then utilized to generate daily SD and SWE datasets for water years 2012–14 using measurements from two networks (COOP and SNOTEL) in the United States. These datasets are used to evaluate daily SD and SWE initializations in NCEP global forecasting models (GFS and CFSv2, both on 0.5° × 0.5° grids) and regional models (NAM on 12 km × 12 km grids and RAP on 13 km × 13 km grids) across eight 2° × 2° boxes. Initialized SD from three models (GFS, CFSv2, and NAM) that utilize Air Force Weather Agency (AFWA) SD data for initialization is 77% below the area-averaged values, on average. RAP initializations, which cycle snow instead of using the AFWA SD, underestimate SD to a lesser degree. Compared with SD errors, SWE errors from GFS, CFSv2, and NAM are larger because of the application of unrealistically low and globally constant snow densities. Furthermore, the widely used daily gridded SD data produced by the Canadian Meteorological Centre (CMC) are also found to underestimate SD (similar to GFS, CFSv2, and NAM), but are worse than RAP. These results suggest an urgent need to improve SD and SWE initializations in these operational models.

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Pepler, Acacia S., Lisa V. Alexander, Jason P. Evans, and Steven C. Sherwood. "Zonal winds and southeast Australian rainfall in global and regional climate models." Climate Dynamics 46, no. 1-2 (March 28, 2015): 123–33. http://dx.doi.org/10.1007/s00382-015-2573-6.

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Rivier, L., Ph Peylin, Ph Ciais, M. Gloor, C. Rödenbeck, C. Geels, U. Karstens, Ph Bousquet, J. Brandt, and M. Heimann. "European CO2 fluxes from atmospheric inversions using regional and global transport models." Climatic Change 103, no. 1-2 (July 15, 2010): 93–115. http://dx.doi.org/10.1007/s10584-010-9908-4.

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Foroutan, Hosein, and Jonathan E. Pleim. "Improving the simulation of convective dust storms in regional-to-global models." Journal of Advances in Modeling Earth Systems 9, no. 5 (September 2017): 2046–60. http://dx.doi.org/10.1002/2017ms000953.

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Safi’i, A. N., Susilo, D. Ramdani, and B. Muslim. "Utilization of Indonesia’s regional ionosphere model to improve the accuracy of GPS measurements to support disaster mitigation studies." IOP Conference Series: Earth and Environmental Science 950, no. 1 (January 1, 2022): 012097. http://dx.doi.org/10.1088/1755-1315/950/1/012097.

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Abstract Ionospheric can cause severe degradation of GPS (Global Positioning System) functionality and decrease coordinate accuracy. Increasing the precision of GPS station coordinates will improve accuracy in many applications. Many applications can use GPS for deformation studies, such as geodynamic studies, active fault studies, volcanic deformation monitoring, land subsidence studies, and hazard mitigation studies. We can use global ionospheric correction to produce better coordinates by utilizing post-processing GPS data. With the increasing number of GPS stations in Indonesia, it is possible to develop regional ionosphere models. This study computes regional ionospheric models from real-time streaming GPS data and uses them in GPS processing. Regional ionospheric models can increase the accuracy of GPS station coordinates by 5% -10% compared to global ionosphere models (igsg and codg).

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