Simulation of the Indian monsoon using the RegCM3–ROMS regional coupled model

A regional coupled atmosphere–ocean model was developed to study the role of air–sea interactions in the simulation of the Indian summer monsoon. The coupled model includes the regional climate model (RegCM3) as atmospheric component and the regional ocean modeling system (ROMS) as oceanic component. The two-way coupled model system exchanges sea surface temperature (SST) from the ocean to the atmospheric model and surface wind stress and energy fluxes from the atmosphere to the ocean model. The coupled model is run for four years 1997, 1998, 2002 and 2003 and the results are compared with observations and atmosphere-only model runs employing Reynolds SSTs as lower boundary condition. It is found that the coupled model captures the main features of the Indian monsoon and simulates a substantially more realistic spatial and temporal distribution of monsoon rainfall compared to the uncoupled atmosphere-only model. The intraseasonal oscillations are also better simulated in the coupled model compared to the atmosphere-only model. These improvements are due to a better representation of the feedbacks between the SST and convection and highlight the importance of air–sea coupling in the simulation of the Indian monsoon.     

SPOM: A regional model of the sub‐polar north Atlantic

Abstract

A regional model of the sub‐polar North Atlantic has been developed for use in process and variability studies of this important high‐latitude area. Open boundary conditions handle connections with the rest of the Atlantic Ocean at 38°N, while buffer zones are used in the northern boundary regions. Extensive testing and experimentation has led to a model which can reproduce major elements of the hydrography and circulation in the region, although limitations exist. A key model feature is the inclusion of a finite volume partial cell topographic representation that significantly improves the structure of the underlying bottom topography. Improvements include a tighter and sharper gyre structure, increased transports, sub‐polar mode water formation sites linked to the topographic slope along the outside of the gyre and a more reasonable representation of Labrador Sea water properties and dispersal pathways. The choice of inflow conditions for the open southern boundary affects the deep western boundary current, as well as the representation of Mediterranean Water, which has a significant effect on Labrador Sea water in the eastern basin.     

Does increasing the spatial resolution of a regional climate model improve the simulated daily precipitation?

Three different resolution (50, 12, and 1.5 km) regional climate model simulations are compared in terms of their ability to simulate moderate and high daily precipitation events over the southern United Kingdom. The convection-permitting 1.5-km simulation is carried out without convective parametrisation. As in previous studies, increasing resolution (especially from 50 to 12 km) is found to improve the representation of orographic precipitation. The 50-km simulation underestimates mean precipitation over the mountainous region of Wales, and event intensity tends to be too weak; this bias is reduced in both the 12- and 1.5-km simulations for both summer and winter. In south–east England lowlands where summer extremes are mostly convective, increasing resolution does not necessary lead to an improvement in the simulation. For the 12-km simulation, simulated daily extreme events are overly intense. Even though the average intensity of summer daily extremes is improved in the 1.5-km simulation, this simulation has a poorer mean bias with too many events exceeding high thresholds. Spatial density and clustering of summer extremes in south–east England are poorly simulated in both the 12- and 1.5-km simulations. In general, we have not found any clear evidence to show that the 1.5-km simulation is superior to the 12-km simulation, or vice versa at the daily level.     

Comment on “application of the Canadian regional climate model to the Laurentian Great Lakes region: Implementation of a lake model”

Abstract

A recently published slab model formulation of lake thermodynamics (Goyette et al., 2000), including an empirical factor to adjust the incoming heat flux so that the modelled lake surface temperature agrees with observed climatology, leads to a distinct lack of energy conservation. The empirical adjustment conceptually represents an exchange of heat between the mixed‐layer water (the slab that is explicitly simulated in the model) and deeper layers of water. It ensures a realistic temporal progression of temperature in the mixed layer, but the thermodynamic balance of the deeper water is not considered. When the deeper water is considered, it is found that the empirical adjustment accounts for the entire heat input to the deeper water, and on an annual mean basis, it is considerably unbalanced. This reveals a flaw in this model concept and, although not entirely invalidating the model, it needs to be included as a caveat in its use.     

Intraseasonal variability in the far-east pacific: investigation of the role of air�Csea coupling in a regional coupled model

Intraseasonal variability in the eastern Pacific warm pool in summer is studied, using a regional ocean?Catmosphere model, a linear baroclinic model (LBM), and satellite observations. The atmospheric component of the model is forced by lateral boundary conditions from reanalysis data. The aim is to quantify the importance to atmospheric deep convection of local air?Csea coupling. In particular, the effect of sea surface temperature (SST) anomalies on surface heat fluxes is examined. Intraseasonal (20?C90?day) east Pacific warm-pool zonal wind and outgoing longwave radiation (OLR) variability in the regional coupled model are correlated at 0.8 and 0.6 with observations, respectively, significant at the 99% confidence level. The strength of the intraseasonal variability in the coupled model, as measured by the variance of outgoing longwave radiation, is close in magnitude to that observed, but with a maximum located about 10° further west. East Pacific warm pool intraseasonal convection and winds agree in phase with those from observations, suggesting that remote forcing at the boundaries associated with the Madden?CJulian oscillation determines the phase of intraseasonal convection in the east Pacific warm pool. When the ocean model component is replaced by weekly reanalysis SST in an atmosphere-only experiment, there is a slight improvement in the location of the highest OLR variance. Further sensitivity experiments with the regional atmosphere-only model in which intraseasonal SST variability is removed indicate that convective variability has only a weak dependence on the SST variability, but a stronger dependence on the climatological mean SST distribution. A scaling analysis confirms that wind speed anomalies give a much larger contribution to the intraseasonal evaporation signal than SST anomalies, in both model and observations. A LBM is used to show that local feedbacks would serve to amplify intraseasonal convection and the large-scale circulation. Further, Hovm?ller diagrams reveal that whereas a significant dynamic intraseasonal signal enters the model domain from the west, the strong deep convection mostly arises within the domain. Taken together, the regional and linear model results suggest that in this region remote forcing and local convection?Ccirculation feedbacks are both important to the intraseasonal variability, but ocean?Catmosphere coupling has only a small effect. Possible mechanisms of remote forcing are discussed.     

An atmosphere–ocean regional climate model for the Mediterranean area: assessment of a present climate simulation

We present an atmosphere–ocean regional climate model for the Mediterranean basin, called the PROTHEUS system, composed by the regional climate model RegCM3 as the atmospheric component and by a regional configuration of the MITgcm model as the oceanic component. The model is applied to an area encompassing the Mediterranean Sea and compared to a stand-alone version of its atmospheric component. An assessment of the model performances is done by using available observational datasets. Despite a persistent bias, the PROTHEUS system is able to capture the inter-annual variability of seasonal sea surface temperature (SST) and also the fine scale spatio-temporal evolution of observed SST anomalies, with spatial correlation as high as 0.7 during summer. The close inspection of a 10-day strong wind event during the summer of 2000 proves the capability of the PROTHEUS system to correctly describe the daily evolution of SST under strong air–sea interaction conditions. As a consequence of the model’s skill in reproducing observed SST and wind fields, we expect a reliable estimation of air–sea fluxes. The model skill in reproducing climatological land surface fields is in line with that of state of the art regional climate models.     

Surface‐absorbed and top‐of‐atmosphere radiation fluxes for the Mackenzie River basin from satellite observations and a regional climate model and an evaluation of the model

Abstract

Both the earth‐reflected shortwave and outgoing longwave radiation (OLR) fluxes at the top of the atmosphere (TOA) as well as surface‐absorbed solar fluxes from Canadian Regional Climate Model (CRCM) simulations of the Mackenzie River Basin for the period March 2000 to September 2003 are compared with the radiation fluxes deduced from satellite observations. The differences between the model and satellite solar fluxes at the TOA and at the surface, which are used in this paper to evaluate the CRCM performance, have opposite biases under clear skies and overcast conditions, suggesting that the surface albedo is underestimated while cloud albedo is overestimated. The slightly larger differences between the model and satellite fluxes at the surface compared to those at the TOA indicate the existence of a small positive atmospheric absorption bias in the model. The persistent overestimation of TOA reflected solar fluxes and underestimation of the surface‐absorbed solar fluxes by the CRCM under all sky conditions are consistent with the overestimation of cloud fraction by the CRCM. This results in a larger shortwave cloud radiative forcing (CRF) both at the TOA and at the surface in the CRCM simulation. The OLR from the CRCM agrees well with the satellite observations except for persistent negative biases during the winter months under all sky conditions. Under clear skies, the OLR is slightly underestimated by the CRCM during the winter months and overestimated in the other months. Under overcast conditions the OLR is underestimated by the CRCM, suggesting an underestimation of cloud‐top temperature by the CRCM. There is an improvement in differences between model and satellite fluxes compared to previously reported results largely because of changes to the treatment of the surface in the model.     

High resolution regional climate model simulations for Germany: part I—validation

A five-member ensemble of regional climate model (RCM) simulations for Europe, with a high resolution nest over Germany, is analysed in a two-part paper: Part I (the current paper) presents the performance of the models for the control period, and Part II presents results for near future climate changes. Two different RCMs, CLM and WRF, were used to dynamically downscale simulations with the ECHAM5 and CCCma3 global climate models (GCMs), as well as the ERA40-reanalysis for validation purposes. Three realisations of ECHAM5 and one with CCCma3 were downscaled with CLM, and additionally one realisation of ECHAM5 with WRF. An approach of double nesting was used, first to an approximately 50 km resolution for entire Europe and then to a domain of approximately 7 km covering Germany and its near surroundings. Comparisons of the fine nest simulations are made to earlier high resolution simulations for the region with the RCM REMO for two ECHAM5 realisations. Biases from the GCMs are generally carried over to the RCMs, which can then reduce or worsen the biases. The bias of the coarse nest is carried over to the fine nest but does not change in amplitude, i.e. the fine nest does not add additional mean bias to the simulations. The spatial pattern of the wet bias over central Europe is similar for all CLM simulations, and leads to a stronger bias in the fine nest simulations compared to that of WRF and REMO. The wet bias in the CLM model is found to be due to a too frequent drizzle, but for higher intensities the distributions are well simulated with both CLM and WRF at the 50 and 7 km resolutions. Also the spatial distributions are close to high resolution gridded observations. The REMO model has low biases in the domain averages over Germany and no drizzle problem, but has a shift in the mean precipitation patterns and a strong overestimation of higher intensities. The GCMs perform well in simulating the intensity distribution of precipitation at their own resolution, but the RCMs add value to the distributions when compared to observations at the fine nest resolution.     

Can ensembles of regional climate model simulations improve results from sensitivity studies?

Intrinsic variability (IV) in regional climate models (RCMs) is often assumed to be small because at climatological timescales, the model solutions tend to be dominated by the model??s lateral boundary conditions. Recent studies have indicated that this IV may actually be large in certain instances for some variables. Direct interpretation of anomalies from RCM sensitivity studies relies on the assumption that differences between model simulations are entirely due to a physical forcing. However, if IV is as large or larger than the physical signal, then this assumption is violated. Using a 20 member ensemble of RCM simulations, we verify that IV of precipitation within a RCM can be large enough to violate the sensitivity study assumption, and we show that generating ensembles of simulations can help reduce the level of IV. We also present two indicators that can rule out the influence of IV when it is ambiguous whether anomalies within a sensitivity study are due to the sensitivity perturbation or whether they are due to IV.     

A scaling analysis of soil moisture–precipitation interactions in a regional climate model

The University of Oklahoma’s Advanced Regional Prediction System (ARPS) was used to examine the impacts of varying mean soil moisture and model resolution on the magnitude and frequency of precipitation events in the U.S. Central Plains and to determine whether modeled soil moisture and precipitation fields exhibit scale invariance using the statistical moments. It was found that high soil moisture resulted in greater precipitation amounts and a higher frequency of events, suggesting the occurrence of a positive soil moisture–precipitation feedback. The scaling analysis performed on cumulative precipitation determined that these fields did not exhibit signs of self-similarity and, therefore, statistical properties cannot be predicted at other resolutions. The scaling properties of soil moisture were highly variable in time which has important implications for the use of remotely sensed data, as scaling properties from 1 day cannot necessarily be applied to subsequent days.     

Application of a “Big-Tree” model to regional climate modeling: a sensitivity study

Summary ¶In order to better understand land-atmosphere interactions and increase the predictability of climate models, it is important to investigate the role of forest representation in climate modeling. Corresponding to the big-leaf model commonly employed in land surface schemes to represent the effects of a forest, a so called big-tree model, which uses multi-layer vegetation to represent the vertical canopy heterogeneity, was introduced and incorporated into the National Center for Atmospheric Research (NCAR) regional climate model RegCM2, to make the vegetation model more physically based. Using this augmented RegCM2 and station data for China during 1991 Meiyu season, we performed 10 experiments to investigate the effects of the application of the big-tree model on the summer monsoon climate.With the big-tree model incorporated into the regional climate model, some climate characteristics, e.g. the 3-month-mean surface temperature, circulation, and precipitation, are significantly and systematically changed over the model domain, and the change of the characteristics differs depending on the area. Due to the better representation of the shading effect in the big-tree model, the temperature of the lower layer atmosphere above the plant canopy is increased, which further influences the 850hPa temperature. In addition, there are significant decreases in the mean latent heat fluxes (within 20–30W/m²) in the three areas of the model domain.The application of the big-tree model influences not only the simulated climate of the forested area, but also that of the whole model domain, and its impact is greater on the lower atmosphere than on the upper atmosphere. The simulated rainfall and surface temperature deviate from the originally simulated result and are (or seem to be) closer to the observations, which implies that an appropriate representation of the big-tree model may improve the simulation of the summer monsoon climate.We also find that the simulated climate is sensitive to some big-tree parameter values and schemes, such as the shape, height, zero-plane displacement height and mixing-length scheme. The simulated local/grid differences may be very large although the simulated areal-average differences may be much lower. The area-average differences in the monthly-mean surface temperature and heat fluxes can amount to 0.5°C and 4W/m², respectively, which correspond to maximum local/grid differences of 3.0°C and 40W/m² respectively. It seems that the simulated climate is most sensitive to the parameter of the zero-plane displacement among the parameters studied.     

Projected climate change scenario over California by a regional ocean–atmosphere coupled model system

This study examines a future climate change scenario over California in a 10-km coupled regional downscaling system of the Regional Spectral Model for the atmosphere and the Regional Ocean Modeling System for the ocean forced by the global Community Climate System Model version 3.0 (CCSM3). In summer, the coupled and uncoupled downscaled experiments capture the warming trend of surface air temperature, consistent with the driving CCSM3 forcing. However, the surface warming change along the California coast is weaker in the coupled downscaled experiment than it is in the uncoupled downscaling. Atmospheric cooling due to upwelling along the coast commonly appears in both the present and future climates, but the effect of upwelling is not fully compensated for by the projected large-scale warming in the coupled downscaling experiment. The projected change of extreme warm events is quite different between the coupled and uncoupled downscaling experiments, with the former projecting a more moderate change. The projected future change in precipitation is not significantly different between coupled and uncoupled downscaling. Both the coupled and uncoupled downscaling integrations predict increased onshore sea breeze change in summer daytime and reduced offshore land breeze change in summer nighttime along the coast from the Bay area to Point Conception. Compared to the simulation of present climate, the coupled and uncoupled downscaling experiments predict 17.5 % and 27.5 % fewer Catalina eddy hours in future climate respectively.     

High resolution regional climate model simulations for Germany: Part II—projected climate changes

The projected climate change signals of a five-member high resolution ensemble, based on two global climate models (GCMs: ECHAM5 and CCCma3) and two regional climate models (RCMs: CLM and WRF) are analysed in this paper (Part II of a two part paper). In Part I the performance of the models for the control period are presented. The RCMs use a two nest procedure over Europe and Germany with a final spatial resolution of 7 km to downscale the GCM simulations for the present (1971–2000) and future A1B scenario (2021–2050) time periods. The ensemble was extended by earlier simulations with the RCM REMO (driven by ECHAM5, two realisations) at a slightly coarser resolution. The climate change signals are evaluated and tested for significance for mean values and the seasonal cycles of temperature and precipitation, as well as for the intensity distribution of precipitation and the numbers of dry days and dry periods. All GCMs project a significant warming over Europe on seasonal and annual scales and the projected warming of the GCMs is retained in both nests of the RCMs, however, with added small variations. The mean warming over Germany of all ensemble members for the fine nest is in the range of 0.8 and 1.3 K with an average of 1.1 K. For mean annual precipitation the climate change signal varies in the range of ?2 to 9 % over Germany within the ensemble. Changes in the number of wet days are projected in the range of ±4 % on the annual scale for the future time period. For the probability distribution of precipitation intensity, a decrease of lower intensities and an increase of moderate and higher intensities is projected by most ensemble members. For the mean values, the results indicate that the projected temperature change signal is caused mainly by the GCM and its initial condition (realisation), with little impact from the RCM. For precipitation, in addition, the RCM affects the climate change signal significantly.     

The 15‐km version of the Canadian regional forecast system

Abstract

A new mesoscale version of the regional forecast system became operational at the Canadian Meteorological Centre on 18 May 2004. The main changes to the regional modelling system include an increase in both the horizontal and vertical resolutions (15‐km horizontal resolution and 58 vertical levels instead of 24‐km resolution and 28 levels) as well as major upgrades to the physics package. The latter consist of a new condensation package, with an improved formulation of the cloudy boundary layer, a new shallow convection scheme based on a Kuo‐type closure, and the Kain and Fritsch deep convection scheme, together with a subgrid‐scale orography parametrization scheme to represent gravity wave drag and low‐level blocking effects. The new forecast system also includes a few changes to the regional data assimilation such as additional radiance data from satellites.

Objective verifications using a series of cases and parallel runs, along with subjective evaluations by CMC meteorologists, indicate significantly improved performance using the new 15‐km resolution forecast system. We can conclude from these verifications that the model exhibits a marked reduction in errors, improved predictability by about 12 hours, better forecasts of precipitation, a significant reduction in the spin‐up time, and a different implicit‐explicit partitioning of precipitation. A number of other features include: sharper precipitation patterns, better representation of trace precipitation, and general improvements of deepening lows and hurricanes. In mountainous regions, several aspects are better represented due to combined higher‐resolution orography and the low‐level blocking term.     

Validating a regional climate model’s downscaling ability for East Asian summer monsoonal interannual variability

Performance of a regional climate model (RCM), WRF, for downscaling East Asian summer season climate is investigated based on 11-summer integrations associated with different climate conditions with reanalysis data as the lateral boundary conditions. It is found that while the RCM is essentially unable to improve large-scale circulation patterns in the upper troposphere for most years, it is able to simulate better lower-level meridional moisture transport in the East Asian summer monsoon. For precipitation downscaling, the RCM produces more realistic magnitude of the interannual variation in most areas of East Asia than that in the reanalysis. Furthermore, the RCM significantly improves the spatial pattern of summer rainfall over dry inland areas and mountainous areas, such as Mongolia and the Tibetan Plateau. Meanwhile, it reduces the wet bias over southeast China. Over Mongolia, however, the performance of precipitation downscaling strongly depends on the year: the WRF is skillful for normal and wet years, but not for dry years, which suggests that land surface processes play an important role in downscaling ability. Over the dry area of North China, the WRF shows the worst performance. Additional sensitivity experiments testing land effects in downscaling suggest the initial soil moisture condition and representation of land surface processes with different schemes are sources of uncertainty for precipitation downscaling. Correction of initial soil moisture using the climatology dataset from GSWP-2 is a useful approach to robustly reducing wet bias in inland areas as well as to improve spatial distribution of precipitation. Despite the improvement on RCM downscaling, regional analyses reveal that accurate simulation of precipitation over East China, where the precipitation pattern is strongly influenced by the activity of the Meiyu/Baiu rainfall band, is difficult. Since the location of the rainfall band is closely associated with both lower-level meridional moisture transport and upper-level circulation structures, it is necessary to have realistic upper-air circulation patterns in the RCM as well as lower-level moisture transport in order to improve the circulation-associated convective rainfall band in East Asia.     

Evaluating regional atmospheric water vapour estimates derived from GPS and short‐range forecasts of the Canadian Global Environmental Multiscale model in southern Alberta

Abstract

Integrated atmospheric moisture has been derived from a network of Global Positioning System (GPS) receivers established in southern Alberta. GPS receivers and post‐processing techniques provide the ability to estimate integrated precipitable water vapour (PWV) at temporal and spatial scales not usually available using conventional observational techniques and without costly expendables. GPS‐derived PWV was evaluated during the Alberta GPS Atmospheric Moisture Evaluation (A‐GAME) using nearby radiosonde observations from the Airdrie, Olds‐Didsbury and Sundre airports during field campaigns in the summers of 2003 and 2004. For the 2004 A‐GAME period, the regional (15 km) Global Environmental Multiscale model (GEM)‐modelled PWV was compared to the GPS derived PWV using a distance weighting approach. GEM model performance was assessed with regards to prognosis time (from 0 to 9 hours), grid cell elevation, location and the presence of storms in the study region. The results show that there is good agreement between radiosonde‐derived PWV and PWV derived from nearby GPS sites with correlations (r²) ranging from 0.76 to 0.84; the GPS‐derived PWV showed a small dry bias averaging 0.6 mm. When compared to GPS‐derived PWV, GEM model performance was found to be favourable out to the hour‐3 prognosis with an overall correlation (r²) of 0.63. Performance decreased with increasing prognosis time and as a result of the presence of storm activity in the study region but did not decrease with increasing grid cell elevation.