Nested Regional Climate Model Research Articles

Multiple regional climate model projections to assess building thermal performance in Brazil: Understanding the uncertainty

Understanding the trends and uncertainties in Building Energy Simulation (BES) performance indicators under future climate conditions is crucial for mitigating issues such as overheating and power outages. To address this, we generated a set of weather files for all 27 state capitals in Brazil, considering six climate model projections (three General Circulation Models as driving models and two nested Regional Climate Models) and two distinct emission scenarios from the CORDEX project. We analyzed the variability in climatic variables and subsequently performed BES on a representative Brazilian social housing unit to evaluate its impact on the performance indicators outcomes. Consistent with previous studies, a substantial increase in cooling-related demands was observed in the more pessimistic scenario (RCP8.5) and mild increases in the more optimistic scenario (RCP2.6), with a trend toward stabilization after 2050. Regarding uncertainties, we found higher Relative Standard Deviation (RSD) values for the cooling degree hours indicator. The capitals in the Central-West, Southeast, and South regions exhibited greater uncertainty regarding temperature indicators, whereas the irradiation parameters displayed higher uncertainties in the Northeast region. For the BES outcomes, RSD values as high as 19.9% were found for cooling load values. It was also demonstrated that locations, periods, and scenarios exhibit different extreme climate model projections. Ideally, employing an ensemble of weather files developed from other models would help assess associated uncertainties in the building performance indicators.

Future Health Risk Assessment of Exposure to PM2.5 in Different Age Groups of Children in Northern Thailand.

Particulate matter with a diameter less than 2.5 (PM2.5) is one of the major threats posed by air pollution to human health. It penetrates the respiratory system, particularly the lungs. In northern Thailand, the PM2.5 concentrations have significantly increased in the past decade, becoming a major concern for the health of children. This study aimed to assess the health risk of PM2.5 in different age groups of children in northern Thailand between 2020 and 2029. Based on the PM2.5 data from the simulation of the Nested Regional Climate Model with Chemistry (NRCM-Chem), the hazard quotient (HQ) was used to estimate the possible risk from PM2.5 exposure in children. In general, all age groups of children in northern Thailand will tend to experience the threat of PM2.5 in the future. In the context of age-related development periods, infants are at a higher risk than other groups (toddlers, young children, school age and adolescents), but adolescents also have a lower risk of exposure to PM2.5, albeit maintaining a high HQ value (>1). Moreover, the analysis of risk assessment in different age groups of children revealed that PM2.5 exposure might indeed affect adolescent risk differently depending on gender, with males generally at a heightened risk than females in adolescence.

Projection of the Precipitation Extremes in Thailand Under Climate Change Scenario RCP8.5

This study explores the predicted extreme precipitation during 2020–2029 in Thailand, using the output of the nested regional climate model (NRCM) with the Representative Concentration Pathway (RCP) 8.5 emission scenario. The research used five different extreme precipitation indices, i.e., annual precipitation total (PRCPTOT), the annual number of rainy days (RD), simple daily intensity index (SDII), consecutive dry days (CDDs), and consecutive wet days (CWD). The PRCTOT was generally located in eastern Thailand, with significant declining trends, while the increasing trend was found in northern Thailand. The quantity of the PRCPTOT varies marginally from 100 to 200 mm per decade. The annual RD was influenced mostly by the negative trends in Thailand. A remarkable trend is a rising of annual SDII, with major statistical increases ranging from 5 to 20%. A widespread decline in CWD was found in most regions. The statistically significant pattern of CWD has been geographically concentrated in the northern, southern, and eastern regions of Thailand. Southern Thailand continues to decline by −10 to −30%. A drastic decline has been observed in the south of Thailand. Projected interannual precipitation variability shows that December–January–February (DJF) variability will be greater than either annual or June–July–August (JJA) variability in most years, with the exception of 2022, 2025, and 2026, in which the JJA variability will be greater than both the annual variability and the DJF variability.

Separating Emission and Meteorological Drivers ofMid‐21st‐Century Air Quality Changes in IndiaBased on Multiyear Global‐RegionalChemistry‐Climate Simulations

AbstractMany Indian metropolitan areas currently suffer from severe air pollution such as PM2.5, which might continue into future decades, dependent on the trends in emission growth and regional climate. Based on a multiyear Nested Regional Climate Model coupled with Chemistry simulation, we developed a daily index (Hazy Weather Index for India, HWII) to characterize the meteorology‐pollution relationship over three heavily polluted cities (Delhi, Kolkata, and Mumbai) and Indo‐Gangetic Plain. HWII consists of near‐surface (10 m) zonal wind (U10) and temperature at 200 hPa (T200) over the northwestern Indian Ocean, and local planetary boundary layer height. The simulated PM2.5 levels during the Historical Period (1997–2004) exhibit robust negative correlation with the HWII. The negative correlation captures day‐to‐day covariability of surface PM2.5 and meteorology, highlighting the role of monsoon‐related large‐scale circulation in redistributing locally emitted pollutants. More importantly, two future (2046–2054) simulations with regional warming under the Representative Concentration Pathway 6.0 and 8.5 were analyzed. The future changes in HWII and the three predictive meteorological variables work in favor of a stronger pollution horizontal dispersion and vertical ventilation and thus could lead to a reduction of PM2.5 level by 7%. The meteorology‐driven reduction in PM2.5, however, is overwhelmed by the projected growth in anthropogenic emission (especially under Representative Concentration Pathway 8.5 emission by 31%). Our results are contrary to previous studies over other regions (e.g., China) where future climate change might contribute to PM2.5 increase.

Projection of near-future climate change and agricultural drought in Mainland Southeast Asia under RCP8.5

In order to plan for agricultural irrigation, the drought risk and amount of water needed for crops must be well studied. In this work, we apply the Standard Precipitation Index (SPI) and Crop Water Need (CWN) using input data from a fine-resolution Nested Regional Climate Model (NRCM) to assess the risk of future agricultural drought in Mainland Southeast Asia from 2020 to 2029. The NRCM was performed with resolutions of 60 and 10-km grid spacing for the present (1990–1999) and the future (2020–2029). The model employs initial and boundary conditions from the Community Climate System Model Version 4 (CCSM4) for meteorological variables. Two simulations, present-day (1990–1999) and future (2020–2029), were conducted under the Representative Concentration Pathway (RCP) 8.5 climate scenario. In general, the comparison between the NRCM predictions and observed data shows that the NRCM reasonably predicts precipitation and 2-m temperature with a high correlation of 0.89–0.98 and index of agreement (IOA) values ranging from 0.76 to 0.95. The future precipitation tends to decrease by (−1)–(1) mm/day, while the temperature will increase by up to 2–3 °C, which are favorable conditions for drought risk. Additionally, the SPI values between (− 1.5) and 0 for both the dry and rainy seasons indicate a high possibility of drought events in the future. There seemed to be some evidence of drought risk in this region, but the calculation of CWN indicates that the region will remain relatively water rich for agriculture.

How Will Air Quality Change in South Asia by 2050?

AbstractExposure to unhealthy air causes millions of premature deaths and damages crops sufficient to feed a large portion of the South Asian population every year. However, little is known about how future air quality in South Asia will respond to changing human activities. Here we examine the combined effect of changes in climate and air pollutant emissions projected by the Representative Concentration Pathways (RCP) 8.5 and RCP6.0 on air quality of South Asia in 2050 using a state‐of‐the‐science Nested Regional Climate model with Chemistry (NRCM‐Chem). RCP8.5 and RCP6.0 are selected to represent scenarios of highest and lowest air pollution in South Asia by 2050. NRCM‐Chem shows the ability to capture observed key features of variability in meteorological parameters, ozone and related gases, and aerosols. NRCM‐Chem results show that surface ozone and particulate matter of less than 2.5 μm in diameter will increase significantly by midcentury in South Asia under the RCP8.5 but remain similar to present day under RCP6.0. No RCP suggest an improvement in air pollution in South Asia by midcentury. Under RCP8.5, the frequency of air pollution events is predicted to increase by 20–120 days per year in 2050 compared to the present‐day conditions, with particulate matter of less than 2.5 μm in diameter predicted to breach the World Health Organization ambient air quality guidelines on an almost daily basis in many parts of South Asia. These results indicate that while the RCP scenarios project a global improvement in air quality, they generally result in degrading air quality in South Asia.

Energetics of transient-eddy and inter-member variabilities in global and regional climate model simulations

Available Enthalpy (AE) and Kinetic Energy (KE) associated with the natural Transient-Eddy (or Time-Variability, TV) and models’ Internal Variability (or Inter-member Variability, IV) are studied using two ensembles of simulations, one from a nested Regional Climate Model (RCM) driven by reanalyses over a regional domain covering eastern North America, and one from a Global Climate Model (GCM), and the Era-interim reanalyses as reference. The fields of TV and IV energies are first examined, both globally and over the regional domain. Results from GCM simulations reveal that GCM TV is similar to that of reanalyses, confirming the realism of the GCM simulations, and TV and IV are approximately equal, in agreement with the ergodicity property. For RCM simulations, TV energies are similar to those of reanalyses driving them. On the other hand, the IV energies of reanalyses-driven RCM simulations are much smaller than those of the GCM, because of the control exerted by the lateral boundary conditions imposed in nested models. While GCM IV energies present similar seasonal variations as the TV energies, the RCM IV greatly fluctuates in time, with short episodes of large variations. The second part of this study is devoted to the analysis of TV and IV energetic budgets. Results indicate similar physical interpretations of conversions, generations and destructions for both TV and IV energetics, although TV is associated with natural phenomena of weather disturbances and IV is a model feature contributing to the uncertainties of simulations.

The roles of bias-correction and resolution in regional climate simulations of summer extremes

The suitability of dynamical downscaling in producing high-resolution climate scenarios for impact assessments is limited by the quality of the driving data and regional climate model (RCM) error. Multiple RCMs driven by a single global climate model simulation of current climate show a reduction in bias compared to the driving data, and the remaining bias motivates exploration of bias correction and higher RCM resolution. The merits of bias correcting the mean climate of the driving data (boundary bias correction) versus bias correcting the mean of the RCM output data are explored and compared to model resolution sensitivity. This analysis focuses on the simulation of summer temperature and precipitation extremes using a single RCM, the Nested Regional Climate Model (NRCM). The NRCM has a general cool bias for hot and cold extremes, a wet bias for wet extremes and a dry bias for dry extremes. Both bias corrections generally reduced the bias and overall error with some indication that boundary bias correction provided greater benefits than bias correcting the mean of the RCM output data, particularly for precipitation. High resolution tended not to lead to further improvements, though further work is needed using multiple resolution evaluation datasets and convection permitting resolution simulations to comprehensively assess the value of high resolution.

Modeling high-impact weather and climate: lessons from a tropical cyclone perspective

Although the societal impact of a weather event increases with the rarity of the event, our current ability to assess extreme events and their impacts is limited by not only rarity but also by current model fidelity and a lack of understanding and capacity to model the underlying physical processes. This challenge is driving fresh approaches to assess high- impact weather and climate. Recent lessons learned in modeling high-impact weather and climate are presented using the case of tropical cyclones as an illustrative example. Through examples using the Nested Regional Climate Model to dynamically downscale large-scale climate data the need to treat bias in the driving data is illustrated. Domain size, location, and resolution are also shown to be critical and should be adequate to: include relevant regional climatephysicalprocesses;resolvekeyimpactparameters;andaccuratelysimulatetheresponse to changes in external forcing. The notion of sufficient model resolution is introduced together with the added value in combining dynamical and statistical assessments to fill out the parent distribution of high-impact parameters.

Assessment of Dynamical Downscaling in Near-Surface Fields with Different Spectral Nudging Approaches Using the Nested Regional Climate Model (NRCM)

AbstractDynamic downscaling with regional-scale climate models is used widely for increasing the spatial resolution of global-scale climate model projections. One uncertainty in generating these projections is the choice of boundary forcing applied. In this study the Nested Regional Climate Model (NRCM) is used with a grid spacing of 12 km over the United States (excluding Hawaii) to dynamically downscale 2.5° National Centers for Environmental Prediction–U.S. Department of Energy Reanalysis-2 data, with different applications of spectral nudging (SN) for the boundary conditions. Nine numerical experiments for July 2005—each with different wavenumbers and nudging duration periods, applied to different model layers—evaluated the performance of SN in downscaling near-surface fields. The calculations were compared with the North America Regional Reanalysis dataset over four subregions of the contiguous 48 states. Results show significant differences with different wavenumbers, nudging duration periods, and nudging altitudes. The short-period SN with three waves, applied above 850 hPa, showed the highest skill in simulating precipitation, whereas whole-period SN produced a higher skill level and performed slightly better than short-period SN for surface temperature and 10-m wind, respectively. Differences in the performance of SN applied at different altitudes were not significant. On the basis of the comparisons for precipitation, surface temperature, and wind fields over entire contiguous states, whole-period nudging with six waves starting above 850 hPa for downscaling calculations for climate-related variables is recommended. This method improved the performance of the NRCM in predicting near-surface fields by more than 30.5% relative to a case with no nudging.

Potential for added value in precipitation simulated by high-resolution nested Regional Climate Models and observations

Regional Climate Models (RCMs) constitute the most often used method to perform affordable high-resolution regional climate simulations. The key issue in the evaluation of nested regional models is to determine whether RCM simulations improve the representation of climatic statistics compared to the driving data, that is, whether RCMs add value. In this study we examine a necessary condition that some climate statistics derived from the precipitation field must satisfy in order that the RCM technique can generate some added value: we focus on whether the climate statistics of interest contain some fine spatial-scale variability that would be absent on a coarser grid. The presence and magnitude of fine-scale precipitation variance required to adequately describe a given climate statistics will then be used to quantify the potential added value (PAV) of RCMs. Our results show that the PAV of RCMs is much higher for short temporal scales (e.g., 3-hourly data) than for long temporal scales (16-day average data) due to the filtering resulting from the time-averaging process. PAV is higher in warm season compared to cold season due to the higher proportion of precipitation falling from small-scale weather systems in the warm season. In regions of complex topography, the orographic forcing induces an extra component of PAV, no matter the season or the temporal scale considered. The PAV is also estimated using high-resolution datasets based on observations allowing the evaluation of the sensitivity of changing resolution in the real climate system. The results show that RCMs tend to reproduce relatively well the PAV compared to observations although showing an overestimation of the PAV in warm season and mountainous regions.

Simulations of precipitation using NRCM and comparisons with satellite observations and CAM: annual cycle

The accurate representation of rainfall in models of global climate has been a challenging task for climate modelers owing to its small space and time scales. Quantifying this variability is important for comparing simulations of atmospheric behavior with real time observations. In this regard, this paper compares both the statistical and dynamically forced aspects of precipitation variability simulated by the high-resolution (36 km) Nested Regional Climate Model (NRCM), with satellite observations from the Tropical Rainfall Measuring Mission (TRMM) 3B42 dataset and simulations from the Community Atmosphere Model (CAM) at T85 spatial resolution. Six years of rainfall rate data (2000–2005) from within the Tropics (30°S–30°N) have been used in the analysis and results are presented in terms of long-term mean rain rates, amplitude and phase of the annual cycle and seasonal mean maps of precipitation. Our primary focus is on characterizing the annual cycle of rainfall over four land regions of the Tropics namely, the Indian Monsoon, the Amazon, Tropical Africa and the North American monsoon. The lower tropospheric circulation patterns are analyzed in both the observations and the models to identify possible causes for biases in the simulated precipitation. The 6-year mean precipitation simulated by both models show substantial biases throughout the global Tropics with NRCM/CAM systematically underestimating/overestimating rainfall almost everywhere. The seasonal march of rainfall across the equator, following the motion of the sun, is clearly seen in the harmonic vector maps. The timing of peak rainfall (phase) produced by NRCM is in closer agreement with the observations compared to CAM. However like the long-time mean, the magnitude of seasonal mean rainfall is greatly underestimated by NRCM throughout the Tropical land mass. Some of these regional biases can be attributed to erroneous circulation and moisture surpluses/deficits in the lower troposphere in both models. Overall, the results seem to indicate that employing a higher spatial resolution (36 km) does not significantly improve simulation of precipitation. We speculate that a combination of several physics parameterizations and lack of model tuning gives rise to the observed differences between NRCM and the observations.

Precipitation Simulations Using WRF as a Nested Regional Climate Model

Abstract This note examines the sensitivity of simulated U.S. warm-season precipitation in the Weather Research and Forecasting model (WRF), used as a nested regional climate model, to variations in model setup. Numerous options have been tested and a few of the more interesting and unexpected sensitivities are documented here. Specifically, the impacts of changes in convective and land surface parameterizations, nest feedbacks, sea surface temperature, and WRF version on mean precipitation are evaluated in 4-month-long simulations. Running the model over an entire season has brought to light some issues that are not otherwise apparent in shorter, weather forecast–type simulations, emphasizing the need for careful scrutiny of output from any model simulation. After substantial testing, a reasonable model setup was found that produced a definite improvement in the climatological characteristics of precipitation over that from the National Centers for Environmental Prediction–National Center for Atmospheric Research global reanalysis, the dataset used for WRF initial and boundary conditions in this analysis.

Challenging some tenets of Regional Climate Modelling

Nested Regional Climate Models (RCMs) are increasingly used for climate-change projections in order to achieve spatial resolutions that would be computationally prohibitive with coupled global climate models. RCMs are commonly thought to behave as a sort of sophisticated magnifying glass to perform dynamical downscaling, which is to add fine-scale details upon the large-scale flow provided as time-dependent lateral boundary condition.

Simulations of Present and Future Climates in the Western United States with Four Nested Regional Climate Models

Abstract In this paper, the authors analyze simulations of present and future climates in the western United States performed with four regional climate models (RCMs) nested within two global ocean–atmosphere climate models. The primary goal here is to assess the range of regional climate responses to increased greenhouse gases in available RCM simulations. The four RCMs used different geographical domains, different increased greenhouse gas scenarios for future-climate simulations, and (in some cases) different lateral boundary conditions. For simulations of the present climate, RCM results are compared to observations and to results of the GCM that provided lateral boundary conditions to the RCM. For future-climate (increased greenhouse gas) simulations, RCM results are compared to each other and to results of the driving GCMs. When results are spatially averaged over the western United States, it is found that the results of each RCM closely follow those of the driving GCM in the same region in both present and future climates. This is true even though the study area is in some cases a small fraction of the RCM domain. Precipitation responses predicted by the RCMs in many regions are not statistically significant compared to interannual variability. Where the predicted precipitation responses are statistically significant, they are positive. The models agree that near-surface temperatures will increase, but do not agree on the spatial pattern of this increase. The four RCMs produce very different estimates of water content of snow in the present climate, and of the change in this water content in response to increased greenhouse gases.

HYDROLOGICAL PROJECTION UNDER THE GLOBAL WARMING IN ASIA WITH A REGIONAL CLIMATE MODEL NESTED IN A GENERAL CIRCULATION MODEL

We have developed a new regional climate model based on the Regional Atmospheric Modeling System (RAMS), and conducted present and future Asian regional climate simulations nested in the CCSR/NIES Atmospheric General Circulation Model (AGCM). The general simulated features of the AGCM and some regional phenomena such as orographic precipitation, which were notappeared in the outcome of the AGCM simulation, were successfully produced by the nested regionalclimate model. Annual mean precipitation, evapotranspiration and surface runoff are projected to increase in a lot of Asian regions. Those indicate faster and stronger hydrological cycles. There is growing apprehension that the risk of floods will increase under the global warming and the associated hydrological changes such as the intensification of convective precipitation in summer, the reduction of snow cover in winter, and earlier snow melting in spring.

Developing perturbations for Climate Change Impact Assessments

Following the 2001 Intergovernmental Panel on Climate Change (IPCC) Third Assessment Report [TAR; IPCC, 2001], and the paucity of climate change impact assessments from developing nations, there has been a significant growth in activities to redress this shortcoming. However, undertaking impact assessments (in relation to malaria, crop stress, regional water supply, etc.) is contingent on available climate‐scale scenarios at time and space scales of relevance to the regional issues of importance. These scales are commonly far finer than even the native resolution of the Global Climate Models (GCMs) (the principal tools for climate change research), let alone the skillful resolution (scales of aggregation at which GCM observational error is acceptable for a given application) of GCMs.Consequently, there is a growing demand for regional‐scale scenarios, which in turn are reliant on techniques to downscale from GCMs, such as empirical downscaling or nested Regional Climate Models (RCMs). These methods require significant skill, experiential knowledge, and computational infrastructure in order to derive credible regional‐scale scenarios. In contrast, it is often the case that impact assessment researchers in developing nations have inadequate resources with limited access to scientists in the broader international scientific community who have the time and expertise to assist. However, where developing effective downscaled scenarios is problematic, it is possible that much useful information can still be obtained for impact assessments by examining the system sensitivity to largerscale climate perturbations. Consequently, one may argue that the early phase of assessing sensitivity and vulnerability should first be characterized by evaluation of the first‐order impacts, rather than immediately addressing the finer, secondary factors that are dependant on scenarios derived through downscaling.

Intraseasonal Oscillation in the Indian Summer Monsoon Simulated by Global and Nested Regional Climate Models

Abstract Simulations of the intraseasonal oscillation (ISO) in the Indian summer monsoon by a general circulation model (GCM) and a nested regional climate model (RCM) are described. The ISO is the leading mode of subseasonal variability in both models. It is shown to be associated with circulation and precipitation anomalies that propagate northward from the equatorial Indian Ocean to the foothills of the Himalayas on the 30–50-day timescale. The spatial structure, timescale, and propagation characteristics of the simulated ISO are found to be similar to those of the leading observed intraseasonal mode. In particular, both of the simulated versions and the observed version all involve periodic deepening and filling of the monsoon trough resulting from northward propagation of troughs and ridges from the equatorial region. Some differences do occur, however: the GCM version of the ISO is too zonally symmetric and the ISO is too strong in both models. During the positive phase of the ISO (i.e., when the IS...

Regional Climate Change Scenarios over the United States Produced with a Nested Regional Climate Model

Abstract In this paper two continuous 3½-year-long climate simulations over the continental United States are discussed, one for present-day conditions and one for conditions under double carbon dioxide concentration, conducted with a limited area model (LAM) nested in a general circulation model (GCM). The models used are a version of the NCAR Community Climate Model (CCM) at rhomboidal 15 spectral resolution and the climate version of the NCAR/Penn State mesoscale model (MM4) at 60-km gridpoint spacing. For present-day conditions the model temperatures are within 1°–2°C of observations except over the Great Lakes region, where temperature is overpredicted by 4°–6°C. The CCM overpredicts precipitation throughout the continental United States (overall by about 60%) and especially over the West (by up to 300%). The nested MM4 overpredicts precipitation over the West but underpredicts it over the eastern United States. In addition, it produces a large amount of topographically and lake-induced sub-GCM grid-...