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Regional Climate Modeling: Advances, Constraints and Use for Adaptation Planning

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A special issue of Climate (ISSN 2225-1154).

Deadline for manuscript submissions: closed (31 October 2015) | Viewed by 86381

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Dr. Aondover Tarhule

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Guest Editor

Department of Geography and Environmental Sustainability, College of Atmospheric & Geographic Sciences, National Weather Center, The University of Oklahoma, Norman, OK 73072, USA
Interests: africa; climate change and climate risk assessment; climate information dissemination and use; drought; hydroclimatic variability; hydrologic response to climate change; water resources.

Dr. Zewdu T. Segele

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Co-Guest Editor

Cooperative Institute for Mesoscale Meteorological Studies The University of Oklahoma 120 David L. Boren Blvd., Suite 2100, Norman, OK 73072-7304, USA
Interests: climate variability; regional climate modeling; statistical prediction; african rainfall variability

Special Issue Information

Dear Colleagues,

Increasingly, Regional Climate Models (RCMs) are being used for regional scale impact assessments and decision making to support climate change adaptation. It is easy to understand the basis for this interest. Compared to Global Climate Models (GCMs), RCMs presently have vastly superior spatial and temporal resolution. Moreover, being dynamic, RCMs simulate climate variables of interest throughout the spatial domain even for those areas where historical data may be lacking. The modeling community is optimistic that RCMs can only get better with time, significantly improving projections of future climate. Not surprisingly, the stakeholder community, used loosely to refer to practitioners interested in using RCMs for mitigation and adaptation planning, continue to express strong demand for RCMs. Spurring such demand is a sense of urgency in their need for regional scale climate projection tools that can be applied to the myriad of water resources, agricultural, ecosystem and other sectors impacted by climate change.

Despite obvious advantages, RCMs do have a number of limitations, including the fact that they generate biases on top of biases inherited from the mother GCMs. Indeed, several international meetings have been convened to explore the recent advances, potentials as well as limitations of regional climate models. Of particular interest to scientists and modelers are the questions of whether RCMs are good enough for use in adaptation and mitigation planning. We can add to these questions: how would we know when RCMs become good enough? What are the major obstacles and what is needed to overcome these obstacles? What should practitioners who chose to apply these models be mindful of to ensure results remain practically meaningful? This issue of Climate Research welcomes your contributions around these and related topics.

Dr. Aondover Tarhule
Dr. Zewdu T. Segele
Guest Editors

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Keywords

adaptation and mitigation planning
bias correction
climate projections
downscaling: dynamical and statistical
general circulation models
regional climate models
- impact assessment
model reliability assessment
- model uncertainty

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Published Papers (11 papers)

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Research

4682 KiB

Open AccessArticle

Application of an Online-Coupled Regional Climate Model, WRF-CAM5, over East Asia for Examination of Ice Nucleation Schemes: Part II. Sensitivity to Heterogeneous Ice Nucleation Parameterizations and Dust Emissions

by Yang Zhang, Ying Chen, Jiwen Fan and Lai-Yung R. Leung

Climate 2015, 3(3), 753-774; https://doi.org/10.3390/cli3030753 - 14 Sep 2015

Cited by 11 | Viewed by 6164

Abstract

Aerosol particles can affect cloud microphysical properties by serving as ice nuclei (IN). Large uncertainties exist in the ice nucleation parameterizations (INPs) used in current climate models. In this Part II paper, to examine the sensitivity of the model predictions to different heterogeneous INPs, WRF-CAM5 simulation using the INP of Niemand et al. (N12) [1] is conducted over East Asia for two full years, 2006 and 2011, and compared with simulation using the INP of Meyers et al. (M92) [2], which is the original INP used in CAM5. M92 calculates the nucleated ice particle concentration as a function of ice supersaturation, while N12 represents the nucleated ice particle concentration as a function of temperature and the number concentrations and surface areas of dust particles. Compared to M92, the WRF-CAM5 simulation with N12 produces significantly higher nucleated ice crystal number concentrations (ICNCs) in the northern domain where dust sources are located, leading to significantly higher cloud ice number and mass concentrations and ice water path, but the opposite is true in the southern domain where temperatures and moistures play a more important role in ice formation. Overall, the simulation with N12 gives lower downward shortwave radiation but higher downward longwave radiation, cloud liquid water path, cloud droplet number concentrations, and cloud optical depth. The increase in cloud optical depth and the decrease in downward solar flux result in a stronger shortwave and longwave cloud forcing, and decreases temperature at 2-m and precipitation. Changes in temperature and radiation lower surface concentrations of OH, O₃, SO₄²⁻, and PM_2.5, but increase surface concentrations of CO, NO₂, and SO₂ over most of the domain. By acting as cloud condensation nuclei (CCN) and IN, dust particles have different impacts on cloud water and ice number concentrations, radiation, and temperature at 2-m and precipitation depending on whether the dominant role of dust is CCN or IN. These results indicate the importance of the heterogeneous ice nucleation treatments and dust emissions in accurately simulating regional climate and air quality. Full article

(This article belongs to the Special Issue Regional Climate Modeling: Advances, Constraints and Use for Adaptation Planning)

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35976 KiB

Open AccessArticle

Application of an Online-Coupled Regional Climate Model, WRF-CAM5, over East Asia for Examination of Ice Nucleation Schemes: Part I. Comprehensive Model Evaluation and Trend Analysis for 2006 and 2011

by Ying Chen, Yang Zhang, Jiwen Fan, Lai-Yung R. Leung, Qiang Zhang and Kebin He

Climate 2015, 3(3), 627-667; https://doi.org/10.3390/cli3030627 - 18 Aug 2015

Cited by 13 | Viewed by 8537

Abstract

Online-coupled climate and chemistry models are necessary to realistically represent the interactions between climate variables and chemical species and accurately simulate aerosol direct and indirect effects on cloud, precipitation, and radiation. In this Part I of a two-part paper, simulations from the Weather Research and Forecasting model coupled with the physics package of Community Atmosphere Model (WRF-CAM5) are conducted with the default heterogeneous ice nucleation parameterization over East Asia for two full years: 2006 and 2011. A comprehensive model evaluation is performed using satellite and surface observations. The model shows an overall acceptable performance for major meteorological variables at the surface and in the boundary layer, as well as column variables (e.g., precipitation, cloud fraction, precipitating water vapor, downward longwave and shortwave radiation). Moderate to large biases exist for cloud condensation nuclei over oceanic areas, cloud variables (e.g., cloud droplet number concentration, cloud liquid and ice water paths, cloud optical depth, longwave and shortwave cloud forcing). These biases indicate a need to improve the model treatments for cloud processes, especially cloud droplets and ice nucleation, as well as to reduce uncertainty in the satellite retrievals. The model simulates well the column abundances of chemical species except for column SO₂ but relatively poor for surface concentrations of several species such as CO, NO₂, SO₂, PM_2.5, and PM₁₀. Several reasons could contribute to the underestimation of major chemical species in East Asia including underestimations of anthropogenic emissions and natural dust emissions, uncertainties in the spatial and vertical distributions of the anthropogenic emissions, as well as biases in meteorological, radiative, and cloud predictions. Despite moderate to large biases in the chemical predictions, the model performance is generally consistent with or even better than that reported for East Asia with only a few exceptions. The model generally reproduces the observed seasonal variations and the difference between 2006 and 2011 for most variables or chemical species. Overall, these results demonstrate promising skills of WRF-CAM5 for long-term simulations at a regional scale and suggest several areas of potential improvements. Full article

(This article belongs to the Special Issue Regional Climate Modeling: Advances, Constraints and Use for Adaptation Planning)

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605 KiB

Open AccessArticle

Hydrological Modeling in Northern Tunisia with Regional Climate Model Outputs: Performance Evaluation and Bias-Correction in Present Climate Conditions

by Asma Foughali, Yves Tramblay, Zoubeida Bargaoui, Julie Carreau and Denis Ruelland

Climate 2015, 3(3), 459-473; https://doi.org/10.3390/cli3030459 - 2 Jul 2015

Cited by 10 | Viewed by 5971

Abstract

This work aims to evaluate the performance of a hydrological balance model in a watershed located in northern Tunisia (wadi Sejnane, 378 km²) in present climate conditions using input variables provided by four regional climate models. A modified version (MBBH) of the lumped and single layer surface model BBH (Bucket with Bottom Hole model, in which pedo-transfer parameters estimated using watershed physiographic characteristics are introduced) is adopted to simulate the water balance components. Only two parameters representing respectively the water retention capacity of the soil and the vegetation resistance to evapotranspiration are calibrated using rainfall-runoff data. The evaluation criterions for the MBBH model calibration are: relative bias, mean square error and the ratio of mean actual evapotranspiration to mean potential evapotranspiration. Daily air temperature, rainfall and runoff observations are available from 1960 to 1984. The period 1960–1971 is selected for calibration while the period 1972–1984 is chosen for validation. Air temperature and precipitation series are provided by four regional climate models (DMI, ARP, SMH and ICT) from the European program ENSEMBLES, forced by two global climate models (GCM): ECHAM and ARPEGE. The regional climate model outputs (precipitation and air temperature) are compared to the observations in terms of statistical distribution. The analysis was performed at the seasonal scale for precipitation. We found out that RCM precipitation must be corrected before being introduced as MBBH inputs. Thus, a non-parametric quantile-quantile bias correction method together with a dry day correction is employed. Finally, simulated runoff generated using corrected precipitation from the regional climate model SMH is found the most acceptable by comparison with runoff simulated using observed precipitation data, to reproduce the temporal variability of mean monthly runoff. The SMH model is the most accurate to reproduce the occurrence of dry days but still underestimates them. From the statistical distribution point of view, corrected SMH precipitation data introduced into the MBBH model were not able to reproduce extreme runoff values generated by observed precipitation data during validation (larger than 80 mm/month). This may be due to the SMH weakness in reproducing moderate and high rainfall levels even after bias correction. This approach may be considered as a way to use regional climate models (RCM) model outputs for studying hydrological impacts. Full article

(This article belongs to the Special Issue Regional Climate Modeling: Advances, Constraints and Use for Adaptation Planning)

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673 KiB

Open AccessArticle

Hydrological Impacts of the Changes in Simulated Rainfall Fields on Nakanbe Basin in Burkina Faso

by Boubacar Ibrahim, Harouna Karambiri and Jan Polcher

Climate 2015, 3(3), 442-458; https://doi.org/10.3390/cli3030442 - 25 Jun 2015

Cited by 12 | Viewed by 5842

Abstract

Changes in rainfall regime during the last five decades over the West African Sahel have significantly modified the hydrological regime of many rivers with a significant impact on water resources. In this study, the main hydrological processes on the Nakanbe watershed in Burkina Faso are described with two hydrological models: GR2M (lumped and monthly model) and ORCHIDEE (ORganising Carbon and Hydrology In Dynamic EcosystEms) (distributed and half hourly model). Both models were calibrated on the watershed from observed runoff data at Wayen outlet (area of 22,000 km²) for the 1978–1999 period. The mean annual hydrological balance components on the watershed over this period are composed of about 4% of runoff, 10% of groundwater recharge and 86% of actual evapotranspiration for both models. An assessment of the hydrological impacts of the changes in rainfall regime simulated by five regional climate models shows some discrepancies. The hydrological simulations show that the hydrological impacts on the water balance of the watershed come mainly from the changes in rainfall field with regard to the frequency and the intensity of rain events. Compared to the decrease in frequency, it appears that the decrease in the intensity of rain events is much more prejudicial to runoff and groundwater recharge. Full article

(This article belongs to the Special Issue Regional Climate Modeling: Advances, Constraints and Use for Adaptation Planning)

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1267 KiB

Open AccessArticle

Detailed Urban Heat Island Projections for Cities Worldwide: Dynamical Downscaling CMIP5 Global Climate Models

by Dirk Lauwaet, Hans Hooyberghs, Bino Maiheu, Wouter Lefebvre, Guy Driesen, Stijn Van Looy and Koen De Ridder

Climate 2015, 3(2), 391-415; https://doi.org/10.3390/cli3020391 - 1 Jun 2015

Cited by 62 | Viewed by 11393

Abstract

A new dynamical downscaling methodology to analyze the impact of global climate change on the local climate of cities worldwide is presented. The urban boundary layer climate model UrbClim is coupled to 11 global climate models contained in the Coupled Model Intercomparison Project 5 archive, conducting 20-year simulations for present (1986–2005) and future (2081–2100) climate conditions, considering the Representative Concentration Pathway 8.5 climate scenario. The evolution of the urban heat island of eight different cities, located on three continents, is quantified and assessed, with an unprecedented horizontal resolution of a few hundred meters. For all cities, urban and rural air temperatures are found to increase strongly, up to 7 °C. However, the urban heat island intensity in most cases increases only slightly, often even below the range of uncertainty. A potential explanation, focusing on the role of increased incoming longwave radiation, is put forth. Finally, an alternative method for generating urban climate projections is proposed, combining the ensemble temperature change statistics and the results of the present-day urban climate. Full article

(This article belongs to the Special Issue Regional Climate Modeling: Advances, Constraints and Use for Adaptation Planning)

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Open AccessArticle

Sensitivity of Horn of Africa Rainfall to Regional Sea Surface Temperature Forcing

by Zewdu T. Segele, Lance M. Leslie and Aondover A. Tarhule

Climate 2015, 3(2), 365-390; https://doi.org/10.3390/cli3020365 - 29 May 2015

Cited by 11 | Viewed by 7353

Abstract

The Abdus Salam International Center for Theoretical Physics (ICTP) version 4.4 Regional Climate Model (RegCM4) is used to investigate the rainfall response to cooler/warmer sea surface temperature anomaly (SSTA) forcing in the Indian and Atlantic Oceans. The effect of SSTA forcing in a specific ocean basin is identified by ensemble, averaging 10 individual simulations in which a constant or linearly zonally varying SSTA is prescribed in individual basins while specifying the 1971–2000 monthly varying climatological sea surface temperature (SST) across the remaining model domain. The nonlinear rainfall response to SSTA amplitude also is investigated by separately specifying +1K, +2K, and +4K SSTA forcing in the Atlantic and Indian Oceans. The simulation results show that warm SSTs over the entire Indian Ocean produce drier conditions across the larger Blue Nile catchment, whereas warming ≥ +2K generates large positive rainfall anomalies exceeding 10 mm·day⁻¹ over drought prone regions of Northeastern Ethiopia. However, the June–September rainy season tends to be wetter (drier) when the SST warming (cooling) is limited to either the Northern or Southern Indian Ocean. Wet rainy seasons generally are characterized by deepening of the monsoon trough, east of 40°E, intensification of the Mascarene high, strengthening of the Somali low level jet and the tropical easterly jet, enhanced zonal and meridional vertically integrated moisture fluxes, and steeply vertically decreasing moist static energy. The opposite conditions hold for dry monsoon seasons. Full article

(This article belongs to the Special Issue Regional Climate Modeling: Advances, Constraints and Use for Adaptation Planning)

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Open AccessArticle

Statistical Seasonal Rainfall Forecast in the Neuquén River Basin (Comahue Region, Argentina)

by Marcela Hebe González

Climate 2015, 3(2), 349-364; https://doi.org/10.3390/cli3020349 - 29 May 2015

Cited by 5 | Viewed by 5946

Abstract

A detailed statistical analysis was performed at the Neuquén river basin using precipitation data for 1980–2007. The hydrological year begins in March with a maximum in June associated with rainfall and another relative maximum in October derived from snow-break. General features of the rainy season and the excess or deficits thereof are analyzed using standardized precipitation index (SPI) for a six-month period in the basin. The SPI has a significant cycle of 14.3 years; the most severe excess (SPI greater than 2) has a return period of 25 years, while the most severe droughts (SPI less than −2) have a return period of 10 years. The SPI corresponding to the rainy season (April–September) (SPI9) has no significant trend and is used to classify wet/dry years. In order to establish the previous circulation patterns associated with interannual SPI9 variability, the composite fields of wet and dry years are compared. There is a tendency for wet (dry) periods to take place during El Niño (La Niña) years, when there are positive anomalies of precipitable water over the basin, when the zonal flow over the Pacific Ocean is weakened (intensified) and/or when there are negative pressure anomalies in the southern part of the country and Antarctic sea. Some prediction schemes using multiple linear regressions were performed. One of the models derived using the forward stepwise method explained 42% of the SPI9 variance and retained two predictors related to circulation over the Pacific Ocean: one of them shows the relevance of the intensity of zonal flow in mid-latitudes, and the other is because of the influence of low pressure near the Neuquén River basin. The cross-validation used to prove model efficiency showed a correlation of 0.41 between observed and estimated SPI9; there was a probability of detection of wet (dry) years of 80% (65%) and a false alarm relation of 25% in both cases. Full article

(This article belongs to the Special Issue Regional Climate Modeling: Advances, Constraints and Use for Adaptation Planning)

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Open AccessArticle

Perspectives for Very High-Resolution Climate Simulations with Nested Models: Illustration of Potential in Simulating St. Lawrence River Valley Channelling Winds with the Fifth-Generation Canadian Regional Climate Model

by Mélissa Cholette, René Laprise and Julie Mireille Thériault

Climate 2015, 3(2), 283-307; https://doi.org/10.3390/cli3020283 - 1 Apr 2015

Cited by 19 | Viewed by 8819

Abstract

With the refinement of grid meshes in regional climate models permitted by the increase in computing power, the grid telescoping or cascade method, already used in numerical weather prediction, can be applied to achieve very high-resolution climate simulations. The purpose of this study is two-fold: (1) to illustrate the perspectives offered by climate simulations on kilometer-scale grid meshes using the wind characteristics in the St. Lawrence River Valley (SLRV) as the test-bench; and (2) to establish some constraints to be satisfied for the physical realism and the computational affordability of these simulations. The cascade method is illustrated using a suite of five one-way nested, time-slice simulations carried out with the fifth-generation Canadian Regional Climate Model, with grid meshes varying from roughly 81 km, successively to 27, 9, 3 and finally 1 km, over domains centered on the SLRV. The results show the added value afforded by very high-resolution meshes for a realistic simulation of the SLRV winds. Kinetic energy spectra are used to document the spin-up time and the effective resolution of the simulations as a function of their grid meshes. A pragmatic consideration is developed arguing that kilometer-scale simulations could be achieved at a reasonable computational cost with time-slice simulations of high impact climate events. This study lends confidence to the idea that climate simulations and projections at kilometer-scale could soon become operationally feasible, thus offering interesting perspectives for resolving features that are currently out of reach with coarser-mesh models. Full article

(This article belongs to the Special Issue Regional Climate Modeling: Advances, Constraints and Use for Adaptation Planning)

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Open AccessArticle

Centered Log-Ratio (clr) Transformation and Robust Principal Component Analysis of Long-Term NDVI Data Reveal Vegetation Activity Linked to Climate Processes

by Muriithi K. Faith

Climate 2015, 3(1), 135-149; https://doi.org/10.3390/cli3010135 - 13 Jan 2015

Cited by 41 | Viewed by 12781

Abstract

Predicting the future climate and its impacts on the global environment is model based, presenting a level of uncertainty. Alternative robust approaches of analyzing high volume climate data to reveal underlying regional and local trends are increasingly incorporating satellite data. This study uses a centered log-ratio (clr) transformation approach and robust principal component analysis (PCA), on a long-term Normalized Difference Vegetation Index (NDVI) dataset to test its applicability in analyzing large multi-temporal data, and potential to recognize important trends and patterns in regional climate. Twenty five years of NDVI data derived by Global Inventory Modeling and Mapping Studies (GIMMS) from 1982 to 2006 were extracted for 88 subwatersheds in central Kenya and statistically analyzed. Untransformed (raw) and clr transformed NDVI data were evaluated using robust PCA. The robust PCA compositional biplots of the clr transformed long-term NDVI data demonstrated the finest spatial-temporal display of observations identifying climate related events that impacted vegetation activity and observed variations in greenness. The responses were interpreted as normal conditions, El Niño Southern Oscillation (ENSO) events of El Niño and La Niña, and drought events known to influence the moisture level and precipitation patterns (high, low, normal) and therefore the level of vegetation greenness (NDVI value). More drought events (4) were observed between 1990 and 2006, a finding corroborated by several authors and linked to increasing climate variability. Results are remarkable, emphasizing the need for appropriate data transformation prior to PCA, dealing with huge complex datasets, to enhance pattern recognition and meaningful interpretation of results. Through improved analysis of past data, uncertainty is decreased in modeling future trends. Full article

(This article belongs to the Special Issue Regional Climate Modeling: Advances, Constraints and Use for Adaptation Planning)

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Open AccessArticle

Model Consistent Pseudo-Observations of Precipitation and Their Use for Bias Correcting Regional Climate Models

by Peter Berg, Thomas Bosshard and Wei Yang

Climate 2015, 3(1), 118-132; https://doi.org/10.3390/cli3010118 - 7 Jan 2015

Cited by 7 | Viewed by 5659

Abstract

Lack of suitable observational data makes bias correction of high space and time resolution regional climate models (RCM) problematic. We present a method to construct pseudo-observational precipitation data bymerging a large scale constrained RCMreanalysis downscaling simulation with coarse time and space resolution observations. The large scale constraint synchronizes the inner domain solution to the driving reanalysis model, such that the simulated weather is similar to observations on a monthly time scale. Monthly biases for each single month are corrected to the corresponding month of the observational data, and applied to the finer temporal resolution of the RCM. A low-pass filter is applied to the correction factors to retain the small spatial scale information of the RCM. The method is applied to a 12.5 km RCM simulation and proven successful in producing a reliable pseudo-observational data set. Furthermore, the constructed data set is applied as reference in a quantile mapping bias correction, and is proven skillful in retaining small scale information of the RCM, while still correcting the large scale spatial bias. The proposed method allows bias correction of high resolution model simulations without changing the fine scale spatial features, i.e., retaining the very information required by many impact models. Full article

(This article belongs to the Special Issue Regional Climate Modeling: Advances, Constraints and Use for Adaptation Planning)

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Self-Reported Experiences of Climate Change in Nigeria: The Role of Personal and Socio-Environmental Factors

by Idowu Ajibade, Frederick Ato Armah, Vincent Kuuire, Isaac Luginaah and Gordon McBean

Climate 2015, 3(1), 16-41; https://doi.org/10.3390/cli3010016 - 23 Dec 2014

Cited by 7 | Viewed by 6700

Abstract

In this study, we examined the individual and socio-environmental factors that mediate differential self-reported experiences of climate change in coastal communities in Lagos, Nigeria. Binary complementary log-log multivariate regression was used to model residents’ experiences of changing rainfall patterns, ocean surges, and flood events. An analysis of both compositional and contextual factors showed that there were urban communities where vulnerability to flooding tends to be clustered, and that this was not fully explained by the characteristics of the people of whom the community was composed. This study, thus, underscores the importance and complex nature of the interaction between personal and socio-environmental determinants in shaping climate change experiences and vulnerability of individuals across coastal neighbourhoods. Key findings suggest certain sub-populations as well as geographic clusters in Lagos require special attention from disaster mitigation experts and policy makers. Full article

(This article belongs to the Special Issue Regional Climate Modeling: Advances, Constraints and Use for Adaptation Planning)

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