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Special Issue "Regional Climate Change and Variability"

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A special issue of Atmosphere (ISSN 2073-4433).

Deadline for manuscript submissions: closed (28 February 2011)

Special Issue Editor

Guest Editor
Prof. Dr. Daniela Jacob

1 Climate Service Center 2.0, Fischertwiete 1, 20095 Hamburg, Germany
2 Max-Planck-Institute for Meteorology, Bundesstrasse 53, 20146 Hamburg, Germany
Website | E-Mail
Fax: +49 404 1173357
Interests: variability in the water cycle; regional climate; land-atmosphere interaction; clouds-aerosols

Special Issue Information

Dear Colleagues,

In many regions climatic changes are visible through for example temperature increase and changes in precipitation pattern. They are mainly initiated through global warming, but often mesoscale phenomena interact regionally with the large scale flow and are therefore able to modify the climate signals in individual regions. Documenting and understanding of these changes as well as the associated processes build the basis for future regional climate change projections. They are achieved using dynamical and statistical downscaling techniques, which have been developed and advanced through the last two decades. Both techniques are needed to establish large ensembles of regional climate change projections, which provide the basis for uncertainty assessments. Regional climate projections utilize global climate change projections and are therefore also connected to so-called emission scenarios or pathways, which provide possible future changes in green house gases and aerosols according to human activity. Information about possible regional climate changes including changes in the mean states as well as changes in extreme events is of utmost importance for society. They can be linked to climate impact models to assess regional climate vulnerability and to develop regional adaptation measures.

This special issue offers an opportunity to publish articles on observed regional climate change, downscaling techniques and future climate change projections in individual regions of the Earth. It is the response to the growing demand on regional climate change information including its uncertainty, and contributes to the dissemination of information needed for impact, adaptation and vulnerability studies.

Prof. Dr. Daniela Jacob
Guest Editor

Keywords

  • observed regional climate change
  • process understanding
  • regional climate modelling
  • regional climate projections dynamical and statistical downscaling
  • uncertainty in regional climate signals
  • change in regional variability
  • changes in extreme events

Published Papers (7 papers)

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Research

Open AccessArticle A Comprehensive Modeling Study on Regional Climate Model (RCM) Application — Regional Warming Projections in Monthly Resolutions under IPCC A1B Scenario
Atmosphere 2012, 3(4), 557-572; doi:10.3390/atmos3040557
Received: 10 August 2012 / Revised: 25 September 2012 / Accepted: 2 October 2012 / Published: 31 October 2012
Cited by 4 | PDF Full-text (1789 KB) | HTML Full-text | XML Full-text
Abstract
Some of the major dimensions of climate change include increase in surface temperature, longer spells of droughts in significant portions of the world, associated higher evapotranspiration rates, and so on. It is therefore essential to comprehend the future possible scenario of climate change
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Some of the major dimensions of climate change include increase in surface temperature, longer spells of droughts in significant portions of the world, associated higher evapotranspiration rates, and so on. It is therefore essential to comprehend the future possible scenario of climate change in terms of global warming. A high resolution limited area Regional Climate Model (RCM) can produce reasonably appropriate projections to be used for climate-scenario generation in country-scale. This paper features the development of future surface temperature projections for Bangladesh on monthly resolution for each year from 2011 to 2100 applying Providing Regional Climates for Impacts Studies (PRECIS), and it explains in detail the modeling processes including the model features, domain size selection, bias identification as well as construction of change field for the concerned climatic variable, in this case, surface temperature. PRECIS was run on a 50 km horizontal grid-spacing under the Intergovernmental Panel on Climate Change (IPCC) A1B scenario and it was found to perform reasonably well in simulating future surface temperature of Bangladesh. The linear regression between observed and model simulated results of monthly average temperatures, within the 30-year period from 1971 to 2000, gives a high correlation of 0.93. The applied change field in average annual temperature shows only 0.5 °C–1 °C deviation from the observed values over the period from 2005 to 2008. Eventually, from the projected average temperature change during the years 1971–2000, it is apparent that warming in Bangladesh prevails invariably every month, which might eventually result in an average annual increase of 4 °C by the year 2100. Calculated anomalies in country-average annual temperature mostly remain on the positive side throughout the period of 2071–2100 indicating an overall up-shift. Apart from these quantitative analyses of temporal changes of temperature, this paper also illustrates their spatial distribution with a view to identify the most vulnerable zones under consequent warming through future times. Full article
(This article belongs to the Special Issue Regional Climate Change and Variability)
Open AccessArticle Modelling Regional Surface Energy Exchange and Boundary Layer Development in Boreal Sweden — Comparison of Mesoscale Model (RAMS) Simulations with Aircraft and Tower Observations
Atmosphere 2012, 3(4), 537-556; doi:10.3390/atmos3040537
Received: 7 July 2012 / Revised: 25 September 2012 / Accepted: 15 October 2012 / Published: 30 October 2012
PDF Full-text (1214 KB) | HTML Full-text | XML Full-text
Abstract
Simulation of atmospheric and surface processes with an atmospheric model (RAMS) during a period of ten days in August 2001 over a boreal area in Sweden were compared to tower measurements and aircraft measurements of vertical profiles as well as surface fluxes from
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Simulation of atmospheric and surface processes with an atmospheric model (RAMS) during a period of ten days in August 2001 over a boreal area in Sweden were compared to tower measurements and aircraft measurements of vertical profiles as well as surface fluxes from low altitude flights. The shape of the vertical profiles was simulated reasonably well by the model although there were significant biases in absolute values. Surface fluxes were less well simulated and the model showed considerable sensitivity to initial soil moisture conditions. The simulations were performed using two different land cover databases, the original one supplied with the RAMS model and the more detailed CORINE database. The two different land cover data bases resulted in relatively large fine scale differences in the simulated values. The conclusion of this study is that RAMS has the potential to be used as a tool to estimate boundary layer conditions and surface fluxes and meteorology over a boreal area but also that further improvement is needed. Full article
(This article belongs to the Special Issue Regional Climate Change and Variability)
Open AccessArticle Climate Variability and Its Impact on Forest Hydrology on South Carolina Coastal Plain, USA
Atmosphere 2011, 2(3), 330-357; doi:10.3390/atmos2030330
Received: 1 March 2011 / Revised: 26 July 2011 / Accepted: 9 August 2011 / Published: 16 August 2011
Cited by 7 | PDF Full-text (1934 KB) | HTML Full-text | XML Full-text
Abstract
Understanding the changes in hydrology of coastal forested wetlands induced by climate change is fundamental for developing strategies to sustain their functions and services. This study examined 60 years of climatic observations and 30 years of hydrological data, collected at the Santee Experimental
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Understanding the changes in hydrology of coastal forested wetlands induced by climate change is fundamental for developing strategies to sustain their functions and services. This study examined 60 years of climatic observations and 30 years of hydrological data, collected at the Santee Experimental Forest (SEF) in coastal South Carolina. We also applied a physically-based, distributed hydrological model (MIKE SHE) to better understand the hydrological responses to the observed climate variability. The results from both observation and simulation for the paired forested watershed systems indicated that the forest hydrology was highly susceptible to change due to climate change. The stream flow and water table depth was substantially altered with a change in precipitation. Both flow and water table level decreased with a rise in temperature. The results also showed that hurricanes substantially influenced the forest hydrological patterns for a short time period (several years) as a result of forest damage. Full article
(This article belongs to the Special Issue Regional Climate Change and Variability)
Open AccessArticle An Ensemble of Arctic Simulations of the AOE-2001 Field Experiment
Atmosphere 2011, 2(2), 146-170; doi:10.3390/atmos2020146
Received: 13 April 2011 / Revised: 29 April 2011 / Accepted: 10 May 2011 / Published: 25 May 2011
Cited by 2 | PDF Full-text (2135 KB) | HTML Full-text | XML Full-text
Abstract
An ensemble of model runs with the COAMPS© regional model is compared to observations in the central Arctic for August 2001 from the Arctic Ocean Experiment 2001 (AOE-2001). The results are from a 6-km horizontal resolution 2nd, inner, nest of the model
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An ensemble of model runs with the COAMPS© regional model is compared to observations in the central Arctic for August 2001 from the Arctic Ocean Experiment 2001 (AOE-2001). The results are from a 6-km horizontal resolution 2nd, inner, nest of the model while the outermost model domain covers the pan-Arctic region, including the marginal ice zone and some of the land areas around the Arctic Ocean. Sea surface temperature and ice cover were prescribed from satellite data while sea-ice surface properties were modeled with an energy balance model, assuming a constant ice thickness. Five ensemble members were generated by altering the initialization time for the innermost nest, the surface roughness and the turbulent mixing scheme for clouds. The large size of the outer domain means that the model simulations have substantial deviations from the observations at synoptic-scale time scales. Therefore the evaluation focuses on statistical measures, rather than in details of individual ensemble member performance as compared directly to observations. In this context, the ensemble members are surprisingly similar even though details differ significantly. The ensemble average results features two main systematic problems: a consistent temperature bias, with too low temperatures below 2–3 km and slightly high temperatures through the rest of the troposphere, and a significant underestimation of the lowest clouds. In terms of total cloud cover, however, the model produces a realistic result; it is the very lowest clouds that are essentially missing. The temperature bias initially appears to be related to an interaction between clouds and radiation; the shape of the mean radiative heating-rate profile is very similar to that of the temperature bias. The lack of the lowest clouds could be due to the too low temperatures in conjunction with a cloud scheme that overestimates the transfer of cloud droplets to ice particles that precipitate. The different terms in the surface energy balance as well as the surface stress has only small systematic errors and are surprisingly consistent between the members. Full article
(This article belongs to the Special Issue Regional Climate Change and Variability)
Open AccessArticle Climate Signals on the Regional Scale Derived with a Statistical Method: Relevance of the Driving Model’s Resolution
Atmosphere 2011, 2(2), 129-145; doi:10.3390/atmos2020129
Received: 14 January 2011 / Revised: 5 April 2011 / Accepted: 16 May 2011 / Published: 23 May 2011
Cited by 9 | PDF Full-text (1732 KB) | HTML Full-text | XML Full-text
Abstract
When assessing the magnitude of climate signals in a regional scale, a host of optional approaches is feasible. This encompasses the use of regional climate models (RCM), nested into global climate models (GCM) for an area of interest as well as employing empirical
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When assessing the magnitude of climate signals in a regional scale, a host of optional approaches is feasible. This encompasses the use of regional climate models (RCM), nested into global climate models (GCM) for an area of interest as well as employing empirical statistical downscaling methods (ESD). In this context the question is addressed: Is an empirical statistical downscaling method capable of yielding results that are comparable to those by dynamical RCMs? Based on the presented ESD method, the comparison of RCM and ESD results show a high amount of agreement. In addition the empirical statistical downscaling can be applied directly to a GCM or a GCM-RCM cascade. The paper aims at comparing the consequences of employing various CGM-RCM-ESD combinations on the derived future changes of temperature and precipitation. This adds to the insight on the scale dependency of downscaling strategies. Results for one GCM with several scenario runs driving several RCMs with and without subsequent empirical statistical downscaling are presented. It is shown that there are only small differences between using the GCM results directly or as a GCM-RCM-ESD cascade. Full article
(This article belongs to the Special Issue Regional Climate Change and Variability)
Open AccessArticle Improving Ammonia Emission Modeling and Inventories by Data Mining and Intelligent Interpretation of the National Air Emission Monitoring Study Database
Atmosphere 2011, 2(2), 110-128; doi:10.3390/atmos2020110
Received: 26 January 2011 / Revised: 16 April 2011 / Accepted: 2 May 2011 / Published: 16 May 2011
Cited by 5 | PDF Full-text (300 KB) | HTML Full-text | XML Full-text
Abstract
Ammonia emission is one of the greatest environmental concerns in sustainable agriculture development. Several limitations and fundamental problems associated with the current agricultural ammonia emission modeling and emission inventories have been identified. They were associated with a significant disconnection between field monitoring data
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Ammonia emission is one of the greatest environmental concerns in sustainable agriculture development. Several limitations and fundamental problems associated with the current agricultural ammonia emission modeling and emission inventories have been identified. They were associated with a significant disconnection between field monitoring data and knowledge about the data. Comprehensive field measurement datasets have not been fully exploited for scientific research and emission regulations. This situation can be considerably improved if the currently available data are better interpreted and the new knowledge is applied to update ammonia emission modeling techniques. The world’s largest agricultural air quality monitoring database with more than 2.4 billion data points has recently been created by the United States’ National Air Emission Monitoring Study. New approaches of data mining and intelligent interpretation of the database are planned to uncover new knowledge and to answer a series of questions that have been raised. The expected results of this new research idea include enhanced fundamental understanding of ammonia emissions from animal agriculture and improved accuracy and scope in regional and national ammonia emission inventories. Full article
(This article belongs to the Special Issue Regional Climate Change and Variability)
Open AccessArticle The Importance of Lateral Boundaries, Surface Forcing and Choice of Domain Size for Dynamical Downscaling of Global Climate Simulations
Atmosphere 2011, 2(2), 67-95; doi:10.3390/atmos2020067
Received: 5 March 2011 / Revised: 4 April 2011 / Accepted: 14 April 2011 / Published: 11 May 2011
Cited by 3 | PDF Full-text (1713 KB) | HTML Full-text | XML Full-text
Abstract
Dynamical downscaling by atmospheric Regional Climate Models (RCMs) forced with low-resolution data should produce climate details and add quality and value to the low-resolution data. The aim of this study was to explore the importance of (i) the oceanic surface forcing (sea-surface temperature
[...] Read more.
Dynamical downscaling by atmospheric Regional Climate Models (RCMs) forced with low-resolution data should produce climate details and add quality and value to the low-resolution data. The aim of this study was to explore the importance of (i) the oceanic surface forcing (sea-surface temperature (SST) and sea-ice), (ii) the lateral boundary condition data, and (iii) the size of the integration domain with respect to improved quality and value in dynamically downscaled data. Experiments addressing the three aspects were performed and the results were investigated for mean sea level pressure (mslp), 2 m air temperature (T2m) and daily precipitation. Although changes in SST gave a clear response locally, changes in the lateral boundary data and the size of the integration domain turned out to be more important with our geographical scope being Norway. The T2m turned out less sensitive to the changes in lateral forcing and the size of the integration domain than mslp and precipitation. The sensitivity for all three variables differed between Norwegian regions; northern parts of Norway were the most sensitive. Even though the sensitivities found in this study might be different in other regions and for other RCMs, these results call for careful consideration when choosing integration domain and driving lateral boundary data when performing dynamical downscaling. Full article
(This article belongs to the Special Issue Regional Climate Change and Variability)

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