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.     

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.     

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.     

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.     

Evaluating climate change at the Croatian Adriatic from observations and regional climate models’ simulations

The 2m temperature (T2m) and precipitation from five regional climate models (RCMs), which participated in the ENSEMBLES project and were integrated at a 25-km horizontal resolution, are compared with observed climatological data from 13 stations located in the Croatian coastal zone. The twentieth century climate was simulated by forcing RCMs with identical boundary conditions from the ERA-40 reanalysis and the ECHAM5/MPI-OM global climate model (GCM); climate change in the twenty-first century is based on the A1B scenario and assessed from the GCM-forced RCMs’ integrations. When forced by ERA-40, most RCMs exhibit cold bias in winter which contributes to an overestimation of the T2m annual cycle amplitude and the errors in interannual variability are in all RCMs smaller than those in the climatological mean. All models underestimate observed warming trends in the period 1951–2010. The largest precipitation biases coincide with locations/seasons with small observed amounts but large precipitation amounts near high orography are relatively well reproduced. When forced by the same GCM all RCMs exhibit a warming in the cold half-year and a cooling (or weak warming) in the warm period, implying a strong impact of GCM boundary forcing. The future eastern Adriatic climate is characterised by a warming, up to +5 °C towards the end of the twenty-first century; for precipitation, no clear signal is evident in the first half of the twenty-first century, but a reduction in precipitation during summer prevails in the second half. It is argued that land-sea contrast and complex coastal configuration of the Croatian coast, i.e. multitude of island and well indented coastline, have a major impact on small-scale variability. Orography plays important role only at small number of coastal locations. We hypothesise that the parameterisations related to land surface processes and soil hydrology have relatively stronger impact on variability than orography at those locations that include a relatively large fraction of land (most coastal stations), but affecting less strongly locations at the Adriatic islands.     

Does the model regional bias affect the projected regional climate change? An analysis of global model projections

An analysis is presented of the dependence of the regional temperature and precipitation change signal on systematic regional biases in global climate change projections. The CMIP3 multi-model ensemble is analyzed over 26 land regions and for the A1B greenhouse gas emission scenario. For temperature, the model regional bias has a negligible effect on the projected regional change. For precipitation, a significant correlation between change and bias is found in about 30% of the seasonal/regional cases analyzed, covering a wide range of different climate regimes. For these cases, a performance-based selection of models in producing climate change scenarios can affect the resulting change estimate, and it is noted that a minimum of four to five models is needed to obtain robust precipitation change estimates. In a number of cases, models with largely different precipitation biases can still produce changes of consistent sign. Overall, it is assessed that in the present generation of models the regional bias does not appear to be a dominant factor in determining the simulated regional change in the majority of cases.     

Multiyear hindcast simulations of summer monsoon over South Asia using a nested regional climate model��BCC_RegCM1.0

Multiyear (1983?C2006) hindcast simulation of summer monsoon over South Asia has been carried out using the regional climate model of the Beijing Climate Centre (BCC_RegCM1.0). The regional climate model (hereafter BCC RCM) is nested into the global climate model of the Beijing Climate Centre BCC_CGCM1.0 (here after CGCM). The regional climate model is initialized on 01 May and integrated up to the end of the September for 24?years. Compared to the driving CGCM the BCC RCM reproduces reasonably well the intensity and magnitude of the large-scale features associated with the South Asia summer monsoon such as the upper level anticyclone at 200?hPa, the mid-tropospheric warming over the Tibetan plateau, the surface heat low and the 850?hPa moisture transport from ocean to the land. Both models, i.e., BCC RCM and the driving CGCM overestimates (underestimates) the 850?hPa southwesterly flow over the northern (southern) Arabian Sea. Moreover, both models overestimate the seasonal mean precipitation over much of the South Asia region compared to the observations. However, the precipitation biases are significantly reduced in the BCC RCM simulations. Furthermore, both models simulate reasonably the interannual variability of the summer monsoon over India. The precipitation index simulated by BCC RCM shows significant correlation (0.62) with the observed one. The BCC RCM simulates reasonably well the spatial and temporal variation of the precipitation and surface air temperature compared to the driving CGCM. Further, the temperature biases are significantly reduced (1?C4°C) in the BCC RCM simulations. The simulated vertical structure of the atmosphere show biases above the four sub-regions, however, these biases are significantly reduced in the BCC RCM simulations compared to the driving CGCM. Compared to the driving CGCM, the evolution processes of the onset of summer monsoon, e.g., the meridional temperature gradient and the vertical wind shear are well simulated by the BCC RCM. The 24-year simulations also show that with a little exception the BCC RCM is capable to reproduce the monsoon active and break phases and the intraseasonal precipitation variation over the Indian subcontinent.     

Impact of soil moisture�Catmosphere coupling on European climate extremes and trends in a regional climate model

Processes acting at the interface between the land surface and the atmosphere have a strong impact on the European summer climate, particularly during extreme years. These processes are to a large extent associated with soil moisture (SM). This study investigates the role of soil moisture?Catmosphere coupling for the European summer climate over the period 1959?C2006 using simulations with a regional climate model. The focus of this study is set on temperature and precipitation extremes and trends. The analysis is based on simulations performed with the regional climate model CLM, driven with ECMWF reanalysis and operational analysis data. The set of experiments consists of a control simulation (CTL) with interactive SM, and sensitivity experiments with prescribed SM: a dry and a wet run to determine the impact of extreme values of SM, as well as experiments with lowpass-filtered SM from CTL to quantify the impact of the temporal variability of SM on different time scales. Soil moisture?Cclimate interactions are found to have significant effects on temperature extremes in the experiments, and impacts on precipitation extremes are also identified. Case studies of selected major summer heat waves reveal that the intraseasonal and interannual variability of SM account for 5?C30% and 10?C40% of the simulated heat wave anomaly, respectively. For extreme precipitation events on the other hand, only the wet-day frequency is impacted in the experiments with prescribed soil moisture. Simulated trends for the past decades, which appear consistent with projected changes for the 21st century, are identified to be at least partly linked to SM-atmosphere feedbacks.     

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.     

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.     

A new building energy model coupled with an urban canopy parameterization for urban climate simulations—part I. formulation, verification, and sensitivity analysis of the model

The generation of heat in buildings, and the way this heat is exchanged with the exterior, plays an important role in urban climate. To analyze the impact on urban climate of a change in the urban structure, it is necessary to build and use a model capable of accounting for all the urban heat fluxes. In this contribution, a new building energy model (BEM) is developed and implemented in an urban canopy parameterization (UCP) for mesoscale models. The new model accounts for: the diffusion of heat through walls, roofs, and floors; natural ventilation; the radiation exchanged between indoor surfaces; the generation of heat due to occupants and equipments; and the consumption of energy due to air conditioning systems. The behavior of BEM is compared to other models used in the thermal analysis of buildings (CBS-MASS, BLAST, and TARP) and with another box-building model. Eventually, a sensitivity analysis of different parameters, as well as a study of the impact of BEM on the UCP is carried out. The validations indicate that BEM provides good estimates of the physical behavior of buildings and it is a step towards a modeling tool that can be an important support to urban planners.     

Regional climate simulations for the European Alpine Region��sensitivity of precipitation to large-scale flow conditions of driving input data

The MM5 modelling system has been used to perform regional climate simulations over Western Europe on a 45-km grid for the years 1971 to 2000. We focus our analysis on the impact of the driving input data on simulated precipitation in the Alpine area. Using ERA40 reanalysis data, the MM5 climatology of precipitation compares reasonably well with an observational climatology for the Alpine region. Switching to an ECHAM5 climate simulation as driving data induces excessive overprediction by up to 80% in the colder seasons there, primarily over the Alpine slopes. The large-scale flow provided by the global datasets revealed moderate differences indicating an increased number of low-pressure systems travelling from the Atlantic into the Alpine region for ECHAM5 compared with ERA40. Mean seasonal 700-hPa wind speeds correspondingly showed higher values for the ECHAM5 driven simulation in the central Alps. Partitioning three-hourly 700-hPa winds according to direction and speed in the central Alps specifically revealed a distinct shift to stronger westerly and north-westerly winds. Furthermore, aggregating three-hourly rainfall amounts to the same wind direction and wind speed intervals as for the wind statistics revealed strongly intensified precipitation due to the overly intense westerly winds, implying too intense orographic precipitation enhancement.     

Can crop albedo be increased through the modification of leaf trichomes,and could this cool regional climate?

Managing the land surface to increase albedo to offset regional warming has received less attention than managing the land surface to sequester carbon. We test whether increasing agricultural albedo can cool regional climate. We first used the Community Atmosphere Model (CAM 3.0) coupled to the Community Land Model (CLM 3.0) to assess the broad climatic effects of a hypothetical implementation of a strategy in which the albedo of cropland regions is increased using high albedo crops. Simulations indicate that planting brighter crops can decrease summertime maximum daily 2 m air temperature by 0.25°C per 0.01 increase in surface albedo at high latitudes (>30°). However, planting brighter crops at low latitudes (<30°) may have negative repercussions including warming the land surface and decreasing precipitation, because increasing the land surface albedo tends to preferentially decrease latent heat fluxes to the atmosphere, which decreases cloud cover and rainfall. We then test a possible method for increasing crop albedo by measuring the range of albedo within 16 isolines of soybeans that differ only with trichome color, orientation, and density but find that such modifications had only minor impacts on leaf albedo. Increasing agricultural albedo may cool high latitude regional climate, but increasing plant albedo sufficiently to offset potential future warming will require larger changes to plant albedo than are currently available.     

Investigation of regional climate models’ internal variability with a ten-member ensemble of 10-year simulations over a large domain

Previous investigations on regional climate models’ (RCM) internal variability (IV) were limited owing to small ensembles, short simulations and small domains. The present work extends previous studies with a ten-member ensemble of 10-year simulations performed with the Canadian Regional Climate Model over a large domain covering North America. The results show that the IV has no long-term tendency but rather fluctuates in time following the synoptic situation within the domain. The IV of mean-sea-level pressure (MSLP) and screen temperature (ST) show a small annual cycle with larger values in spring, which differs from previous studies. For precipitation (PCP), the IV shows a clear annual cycle with larger values in summer, as previously reported. The 10-year climatology of the IV for MSLP and ST shows a well-defined spatial distribution with larger values in the northeast of the domain, near the outflow boundary. A comparison of the IV of MSLP and ST in summer with the transient-eddy variance reveals that the IV is close to its maximum in a small region near the outflow boundary. Same analysis for PCP in summer shows that the IV reaches its maximum in most parts of the domain, except for a small region on the western side near the inflow boundary. Finally, a comparison of the 10-year climate of each simulation of the ensemble showed that the IV may have a significant impact on the climatology of some variables.     

A coupled atmosphere‐ocean model for transient climate change studies

Abstract

A new coupled atmosphere‐ocean model has been developed for climate predictions at decade to century scales. The atmospheric model is similar to that of Hansen et al. (1983) except that the atmospheric dynamic equations for mass and momentum are solved using Arakawa and Lamb's (1977) C grid scheme and the advection of potential enthalpy and water vapour uses the linear upstream scheme (Russell and Lerner, 1981). The new global ocean model conserves mass, allows for divergent flow, has a free surface and uses the linear upstream scheme for the advection of potential enthalpy and salt. Both models run at 4° × 5° resolution, with 9 vertical layers for the atmosphere and 13 layers for the ocean. Twelve straits are included, allowing for subgrid‐scale water flow. Runoff from land is routed into appropriate ocean basins. Atmospheric and oceanic surface fluxes are of opposite sign and are applied synchronously. Flux adjustments are not used. Except for partial strength alternating binomial filters (Shapiro, 1970), which are applied to the momentum components in the atmosphere and oceans, there is no explicit horizontal diffusion.

A 120‐year simulation of the coupled model starting from the oceanic initial conditions of Levitus (1982) is discussed. The model dynamics stabilize after several decades. The maximum northward ocean heat flux is 1.4 × 10¹⁵ W at 16°N. The model appears to maintain the vertical gradients characterizing the separation between the upper and deep ocean spheres. Inadequacies in the coupled model simulation lead to decreasing temperature and salinity in the high latitude North Atlantic and to a poor simulation of the northern North Atlantic thermohaline circulation. The mass transport of the Gulf Stream is about half of observed values, while the transports of the Kuroshio and Antarctic Circumpolar Currents are similar to observations. Additional deficiencies include a climate drift in the surface air temperature of 0.006°C year^‐1 due to a radiation imbalance of 7.4 Wm^‐2 at the top of the atmosphere and too warm temperatures in the eastern portions of tropical oceans. The coupled model should be useful for delineating modelling capabilities without the use of flux adjustments and should serve as a benchmark for future model improvements.     

Multi-century simulations with the coupled GISS–HYCOM climate model: control experiments

Multi-century climate simulations obtained with the GISS atmospheric general circulation model coupled to the hybrid-isopycnic ocean model HYCOM are described. Greenhouse gas concentrations are held fixed in these experiments to investigate the coupled model’s ability to reproduce the major features of today’s climate with minimal drift. Emphasis is placed on the realism of the oceanic general circulation and its effect on air–sea exchange processes. Several model runs using different closures for turbulent vertical exchange as well as improvements to reduce vertical numerical diffusion are compared with climate observations. As in previous studies, the Southern Ocean emerges as the Achilles Heel of the ocean model; deficiencies in its physical representation had far-reaching consequences in early experiments with the coupled model and have provided the strongest impetus for model improvement. The overarching goal of this work is to add diversity to the pool of ocean models available for climate prediction and thereby reduce biases that may stand in the way of assessing climate prediction uncertainty.

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