Evaluation of the regional climate model ALADIN to simulate the climate over North America in the CORDEX framework

In this study, an ensemble of four multi-year climate simulations is performed with the regional climate model ALADIN to evaluate its ability to simulate the climate over North America in the CORDEX framework. The simulations differ in their driving fields (ERA-40 or ERA-Interim) and the nudging technique (with or without large-scale nudging). The validation of the simulated 2-m temperature and precipitation with observationally-based gridded data sets shows that ALADIN performs similarly to other regional climate models that are commonly used over North America. Large-scale nudging improves the temporal correlation of the atmospheric circulation between ALADIN and its driving field, and also reduces the warm and dry summer biases in central North America. The differences between the simulations driven with different reanalyses are small and are likely related to the regional climate model’s induced internal variability. In general, the impact of different driving fields on ALADIN is smaller than that of large-scale nudging. The analysis of the multi-year simulations over the prairie and the east taiga indicates that the ALADIN 2-m temperature and precipitation interannual variability is similar or larger than that observed. Finally, a comparison of the simulations with observations for the summer 1993 shows that ALADIN underestimates the flood in central North America mainly due to its systematic dry bias in this region. Overall, the results indicate that ALADIN can produce a valuable contribution to CORDEX over North America.     

Simulating the impact of the large-scale circulation on the 2-m temperature and precipitation climatology

The impact of the simulated large-scale atmospheric circulation on the regional climate is examined using the Weather Research and Forecasting (WRF) model as a regional climate model. The purpose is to understand the potential need for interior grid nudging for dynamical downscaling of global climate model (GCM) output for air quality applications under a changing climate. In this study we downscale the NCEP-Department of Energy Atmospheric Model Intercomparison Project (AMIP-II) Reanalysis using three continuous 20-year WRF simulations: one simulation without interior grid nudging and two using different interior grid nudging methods. The biases in 2-m temperature and precipitation for the simulation without interior grid nudging are unreasonably large with respect to the North American Regional Reanalysis (NARR) over the eastern half of the contiguous United States (CONUS) during the summer when air quality concerns are most relevant. This study examines how these differences arise from errors in predicting the large-scale atmospheric circulation. It is demonstrated that the Bermuda high, which strongly influences the regional climate for much of the eastern half of the CONUS during the summer, is poorly simulated without interior grid nudging. In particular, two summers when the Bermuda high was west (1993) and east (2003) of its climatological position are chosen to illustrate problems in the large-scale atmospheric circulation anomalies. For both summers, WRF without interior grid nudging fails to simulate the placement of the upper-level anticyclonic (1993) and cyclonic (2003) circulation anomalies. The displacement of the large-scale circulation impacts the lower atmosphere moisture transport and precipitable water, affecting the convective environment and precipitation. Using interior grid nudging improves the large-scale circulation aloft and moisture transport/precipitable water anomalies, thereby improving the simulated 2-m temperature and precipitation. The results demonstrate that constraining the RCM to the large-scale features in the driving fields improves the overall accuracy of the simulated regional climate, and suggest that in the absence of such a constraint, the RCM will likely misrepresent important large-scale shifts in the atmospheric circulation under a future climate.     

Impact of spectral nudging on the downscaling of tropical cyclones in regional climate simulations

This study investigated the simulations of three months of seasonal tropical cyclone (TC) activity over the western North Pacific using the Advanced Research WRF Model. In the control experiment (CTL), the TC frequency was considerably overestimated. Additionally, the tracks of some TCs tended to have larger radii of curvature and were shifted eastward. The large-scale environments of westerly monsoon flows and subtropical Pacific highs were unreasonably simulated. The overestimated frequency of TC formation was attributed to a strengthened westerly wind field in the southern quadrants of the TC center. In comparison with the experiment with the spectral nudging method, the strengthened wind speed was mainly modulated by large-scale flow that was greater than approximately 1000 km in the model domain. The spurious formation and undesirable tracks of TCs in the CTL were considerably improved by reproducing realistic large-scale atmospheric monsoon circulation with substantial adjustment between large-scale flow in the model domain and large-scale boundary forcing modified by the spectral nudging method. The realistic monsoon circulation took a vital role in simulating realistic TCs. It revealed that, in the downscaling from large-scale fields for regional climate simulations, scale interaction between model-generated regional features and forced large-scale fields should be considered, and spectral nudging is a desirable method in the downscaling method.     

Atmospheric and terrestrial water budgets: sensitivity and performance of configurations and global driving data for long term continental scale WRF simulations

Driving data and physical parametrizations can significantly impact the performance of regional dynamical atmospheric models in reproducing hydrometeorologically relevant variables. Our study addresses the water budget sensitivity of the Weather Research and Forecasting Model System WRF (WRF-ARW) with respect to two cumulus parametrizations (Kain–Fritsch, Betts–Miller–Janji?), two global driving reanalyses (ECMWF ERA-INTERIM and NCAR/NCEP NNRP), time variant and invariant sea surface temperature and optional gridded nudging. The skill of global and downscaled models is evaluated against different gridded observations for precipitation, 2 m-temperature, evapotranspiration, and against measured discharge time-series on a monthly basis. Multi-year spatial deviation patterns and basin aggregated time series are examined for four globally distributed regions with different climatic characteristics: Siberia, Northern and Western Africa, the Central Australian Plane, and the Amazonian tropics. The simulations cover the period from 2003 to 2006 with a horizontal mesh of 30 km. The results suggest a high sensitivity of the physical parametrizations and the driving data on the water budgets of the regional atmospheric simulations. While the global reanalyses tend to underestimate 2 m-temperature by 0.2–2 K, the regional simulations are typically 0.5–3 K warmer than observed. Many configurations show difficulties in reproducing the water budget terms, e.g. with long-term mean precipitation biases of 150 mm month^?1 and higher. Nevertheless, with the water budget analysis viable setups can be deduced for all four study regions.     

Validation of the nesting technique in a regional climate model and sensitivity tests to the resolution of the lateral boundary conditions during summer

The ability of a regional climate model (RCM) to successfully reproduce the fine-scale features of a regional climate during summer is evaluated using an approach nick-named the “Big-Brother Experiment” (BBE). The BBE establishes a reference virtual-reality climate with a RCM applied on a large and high-resolution domain: this simulation is called the Big-Brother (BB) simulation. This reference simulation is then downgraded by filtering small-scale features that are unresolved in today’s global objective analyses. The resulting fields are then used as nesting data to drive the same RCM, which is integrated, at the same high resolution as the BB, only over a sub-area of the larger BB domain, hence, producing the Little-Brother simulation (LB). With the BBE approach, differences between the two simulated climates (BB and LB) can be unambiguously attributed to errors associated with the dynamical downscaling technique, and not to model errors or observational limitations. The current study focuses on the summer over the West Coast of North America. Results of the stationary and transient parts of the fields, decomposed by horizontal scales, are presented for the month of July, for 5 consecutive years (1990–1994). Three degrees of spatial filtering (roughly equivalent to the global spectral resolution of T30, T60 and T360) as well as two update intervals (3 and 6 h) of the lateral boundary conditions (LBC) have been employed. This study establishes that the maximum acceptable resolution of driving data for summer is T30, with improved results employing the T60 resolution of LBC. There is little improvement by reducing the time interval from 6 h to 3 h. These results are generally in agreement with previous studies carried out for winter. The good correlation between LB and BB simulations is more difficult to achieve during the summer season, mostly due to weaker control exerted by LBC. Poor correlations are more pronounced for the transient parts than they are for the stationary parts of the fields. This is especially true for the precipitation field, where differences can be attributed to higher temporal variability during the summer due to the presence of convection.     

Ability of an ensemble of regional climate models to reproduce weather regimes over Europe-Atlantic during the period 1961–2000

One of the main concerns in regional climate modeling is to which extent limited-area regional climate models (RCM) reproduce the large-scale atmospheric conditions of their driving general circulation model (GCM). In this work we investigate the ability of a multi-model ensemble of regional climate simulations to reproduce the large-scale weather regimes of the driving conditions. The ensemble consists of a set of 13 RCMs on a European domain, driven at their lateral boundaries by the ERA40 reanalysis for the time period 1961–2000. Two sets of experiments have been completed with horizontal resolutions of 50 and 25 km, respectively. The spectral nudging technique has been applied to one of the models within the ensemble. The RCMs reproduce the weather regimes behavior in terms of composite pattern, mean frequency of occurrence and persistence reasonably well. The models also simulate well the long-term trends and the inter-annual variability of the frequency of occurrence. However, there is a non-negligible spread among the models which is stronger in summer than in winter. This spread is due to two reasons: (1) we are dealing with different models and (2) each RCM produces an internal variability. As far as the day-to-day weather regime history is concerned, the ensemble shows large discrepancies. At daily time scale, the model spread has also a seasonal dependence, being stronger in summer than in winter. Results also show that the spectral nudging technique improves the model performance in reproducing the large-scale of the driving field. In addition, the impact of increasing the number of grid points has been addressed by comparing the 25 and 50 km experiments. We show that the horizontal resolution does not affect significantly the model performance for large-scale circulation.     

Evaluation of Precipitation Indices over North America from Various Configurations of Regional Climate Models

This study presents the evaluation of simulations from two new Canadian regional climate models (RCMs), CanRCM4 and CRCM5, with a focus on the models’ skill in simulating daily precipitation indices and the Standardized Precipitation Index (SPI). The evaluation was carried out over the past two decades using several sets of gridded observations that partially cover North America. The new Canadian RCMs were also compared with four reanalysis products and six other RCMs. The different configurations of the Canadian RCM simulations also permit evaluation of the impact of different spatial resolutions, atmospheric drivers, and nudging conditions. The results from the new Canadian models show some improvement in precipitation characteristics over the previous Canadian RCM (CRCM4), but these differ with the seasons. For winter, CanRCM4 and CRCM5 have better skill than most other models over all of North America. For the summer, CRCM5 0.44° performs best over the United States, while CRCM4 has the best skill over Canada. Good skill is exhibited by CanRCM4 and CRCM4 in simulating the 6-month SPI over the Prairies and the western US Corn Belt. In general, differences are small between runs with or without large-scale spectral nudging; differences are small when different boundary conditions are used.     

Downscaling ability of one-way nested regional climate models: the Big-Brother Experiment

A methodology is developed for testing the downscaling ability of nested regional climate models (RCMs). The proposed methodology, nick-named the Big-Brother Experiment (BBE), is based on a "perfect-prognosis" approach and hence does not suffer from model errors nor from limitations in observed climatologies. The BBE consists in first establishing a reference climate by performing a large-domain high-resolution RCM simulation: this simulation is called the Big Brother. This reference simulation is then degraded by filtering short scales that are unresolved in today's global objective analyses (OA) and/or global climate models (GCMs) when integrated for climate projections. This filtered reference is then used to drive the same nested RCM (called the Little Brother), integrated at the same high-resolution as the Big Brother, but over a smaller domain that is embedded in the Big-Brother domain. The climate statistics of the Little Brother are then compared with those of the Big Brother over the Little-Brother domain. Differences can thus be attributed unambiguously to errors associated with the nesting and downscaling technique, and not to model errors nor to observation limitations. The results of the BBE applied to a one-winter-month simulation over eastern North America at 45-km grid-spacing resolution show that the one-way nesting strategy has skill in downscaling large-scale information to the regional scales. The time mean and variability of fine-scale features in a number of fields, such as sea level pressure, 975-hPa temperature and precipitation are successfully reproduced, particularly over regions where small-scale surface forcings are strong. Over other regions such as the ocean and away from the surface, the small-scale reproducibility is more difficult to achieve.     

Dynamical downscaling of ERA-40 in complex terrain using the WRF regional climate model

Results from a first-time employment of the WRF regional climate model to climatological simulations in Europe are presented. The ERA-40 reanalysis (resolution 1°) has been downscaled to a horizontal resolution of 30 and 10?km for the period of 1961?C1990. This model setup includes the whole North Atlantic in the 30?km domain and spectral nudging is used to keep the large scales consistent with the driving ERA-40 reanalysis. The model results are compared against an extensive observational network of surface variables in complex terrain in Norway. The comparison shows that the WRF model is able to add significant detail to the representation of precipitation and 2-m temperature of the ERA-40 reanalysis. Especially the geographical distribution, wet day frequency and extreme values of precipitation are highly improved due to the better representation of the orography. Refining the resolution from 30 to 10?km further increases the skill of the model, especially in case of precipitation. Our results indicate that the use of 10-km resolution is advantageous for producing regional future climate projections. Use of a large domain and spectral nudging seems to be useful in reproducing the extreme precipitation events due to the better resolved synoptic scale features over the North Atlantic, and also helps to reduce the large regional temperature biases over Norway. This study presents a high-resolution, high-quality climatological data set useful for reference climate impact studies.     

Impacts of Spectral Nudging on the Sensitivity of a Regional Climate Model to Convective Parameterizations in East Asia

The sensitivity of a regional climate model (RCM) to cumulus parameterization (CUPA) schemes in modeling summer precipitation over East Asia has been investigated by using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (PSU-NCAR MM5). The feasibility of physical ensemble and the effect of interior (spectral) nudging are also assessed. The RCM simulations are evaluated against the NCEP/NCAR reanalysis data and NCEP/CPC precipitation data for three summers (JJA) in 1991, 1998, and 2003. The results show that the RCM is highly sensitive to CUPA schemes. Different CUPA schemes cause distinctive characteristics in the modeling of JJA precipitation and the intraseasonal (daily) variability of regional precipitation. The sensitivity of the RCM simulations to the CUPA schemes is reduced by adopting the spectral nudging technique, which enables the RCM to reproduce more realistic large-scale circulations at the upper levels of the atmosphere as well as near the surface, and better precipitation simulation in the selected experiments. The ensemble simulations using different CUPA schemes show higher skills than individual members for both control runs and spectral nudging runs. The physical ensemble adopting the spectral nudging technique shows the highest downscaling skill in capturing the general circulation patterns for all experiments and improved temporal distributions of precipitation in some regions.     

Spectral nudging sensitivity experiments in a regional climate model

In this study, the scale selective bias correction (SSBC) method described by Kanamitsu et al. (2010) is further examined by considering the full wind nudging and the vertically weighted damping coefficient. The 2001 June?CJuly?CAugust RSM simulation over a relatively large domain covering much of the Asian continent, the northern part of Australia, and the Indian and western Pacific oceans was the main focus. The full wind nudging shows wind fields closer to the driving global analysis. However, it leads to significantly distorted fields (e.g., temperature and geopotential height) aloft, accompanying excessive precipitation over the western Pacific. The gradual reduction of vorticity nudging from the model top to the ground surface improves rainfall patterns without a discernible distortion of large-scale fields. Further evaluation of a 10-year-summer simulation over East Asia confirmed that this revised SSBC method improves the monsoonal rainfall against the method of Kanamitsu et al. It is therefore concluded that vorticity nudging alleviates largescale errors by maintaining the near geostrophic balance between mass and winds. The reduction of this nudging factor in the lower troposphere allows the ageostrophic component of wind to develop as in nature, which leads to the improvement of precipitation.     

Testing the downscaling ability of a one-way nested regional climate model in regions of complex topography

The downscaling ability of a one-way nested regional climate model (RCM) is evaluated over a region subjected to strong surface forcing: the west of North America. The sensitivity of the results to the horizontal resolution jump and updating frequency of the lateral boundary conditions are also evaluated. In order to accomplish this, a perfect-model approach nicknamed the Big-Brother Experiment (BBE) was followed. The experimental protocol consists of first establishing a virtual-reality reference climate over a fairly large area by using the Canadian RCM with grid spacing of 45 km nested within NCEP analyses. The resolution of the simulated climate is then degraded to resemble that of operational general circulation models (GCM) or observation analyses by removing small scales; the filtered fields are then used to drive the same regional model, but over a smaller sub-area. This set-up permits a comparison between two simulations of the same RCM over a common region. The Big-Brother Experiment has been carried out for four winter months over the west coast of North America. The results show that complex topography and coastline have a strong positive impact on the downscaling ability of the one-way nesting technique. These surface forcings, found to be responsible for a large part of small-scale climate features, act primarily locally and yield good climate reproducibility. Precipitation over the Rocky Mountains region is a field in which such effect is found and for which the nesting technique displays significant downscaling ability. The best downscaling ability is obtained when the ratio of spatial resolution between the nested model and the nesting fields is less than 12, and when the update frequency is more than twice a day. Decreasing the spatial resolution jump from a ratio of 12 to six has more benefits on the climate reproducibility than a reduction of spatial resolution jump from two to one. Also, it is found that an update frequency of four times a day leads to a better downscaling than twice a day when a ratio of spatial resolution of one is used. On the other hand, no improvement was found by using high-temporal resolution when the driving fields were degraded in terms of spatial resolution.     

Understanding the West African monsoon variability and its remote effects: an illustration of the grid point nudging methodology

General circulation models still show deficiencies in simulating the basic features of the West African Monsoon at intraseasonal, seasonal and interannual timescales. It is however, difficult to disentangle the remote versus regional factors that contribute to such deficiencies, and to diagnose their possible consequences for the simulation of the global atmospheric variability. The aim of the present study is to address these questions using the so-called grid point nudging technique, where prognostic atmospheric fields are relaxed either inside or outside the West African Monsoon region toward the ERA40 reanalysis. This regional or quasi-global nudging is tested in ensembles of boreal summer simulations. The impact is evaluated first on the model climatology, then on intraseasonal timescales with an emphasis on North Atlantic/Europe weather regimes, and finally on interannual timescales. Results show that systematic biases in the model climatology over West Africa are mostly of regional origin and have a limited impact outside the domain. A clear impact is found however on the eddy component of the extratropical circulation, in particular over the North Atlantic/European sector. At intraseasonal timescale, the main regional biases also resist to the quasi-global nudging though their magnitude is reduced. Conversely, nudging the model over West Africa exerts a strong impact on the frequency of the two North Atlantic weather regimes that favor the occurrence of heat waves over Europe. Significant impacts are also found at interannual timescale. Not surprisingly, the quasi-global nudging allows the model to capture the variability of large-scale dynamical monsoon indices, but exerts a weaker control on rainfall variability suggesting the additional contribution of regional processes. Conversely, nudging the model toward West Africa suppresses the spurious ENSO teleconnection that is simulated over Europe in the control experiment, thereby emphasizing the relevance of a realistic West African monsoon simulation for seasonal prediction in the extratropics. Further experiments will be devoted to case studies aiming at a better understanding of regional processes governing the monsoon variability and of the possible monsoon teleconnections, especially over Europe.     

The impact of lateral boundary data errors on the simulated climate of a nested regional climate model

In this study, we investigate the response of a Regional Climate Model (RCM) to errors in the atmospheric data used as lateral boundary conditions (LBCs) using a perfect-model framework nick-named the “Big-Brother Experiment” (BBE). The BBE has been designed to evaluate the errors due to the nesting process excluding other model errors. First, a high-resolution (45 km) RCM simulation is made over a large domain. This simulation, called the Perfect Big Brother (PBB), is driven by the National Centres for Environmental Prediction (NCEP) reanalyses; it serves as reference virtual-reality climate to which other RCM runs will be compared. Next, errors of adjustable magnitude are introduced by performing RCM simulations with increasingly larger domains at lower horizontal resolution (90 km mesh). Such simulations with errors typical of today’s Coupled General Circulation Models (CGCM) are called the Imperfect Big-Brother (IBB) simulations. After removing small scales in order to achieve low-resolution typical of today’s CGCMs, they are used as LBCs for driving smaller domain high-resolution RCM runs; these small-domain high-resolution simulations are called Little-Brother (LB) simulations. The difference between the climate statistics of the IBB and those of PBB simulations mimic errors of the driving model. The comparison of climate statistics of the LB to those of the PBB provides an estimate of the errors resulting solely from nesting with imperfect LBCs. The simulations are performed over the East Coast of North America using the Canadian RCM, for five consecutive February months (from 1990 to 1994). It is found that the errors contained in the large scales of the IBB driving data are transmitted to and reproduced with little changes by the LB. In general, the LB restores a great part of the IBB small-scale errors, even if they do not take part in the nesting process. The small scales are seen to improve slightly in regions with important orographic forcing due to the finer resolution of the RCM. However, when the large scales of the driving model have errors, the small scales developed by the LB have errors as well, suggesting that the large scales precondition the small scales. In order to obtain correct small scales, it is necessary to provide the accurate large-scale circulation at the lateral boundary of the RCM.     

Testing the downscaling ability of a one-way nested regional climate model in regions of complex topography

The downscaling ability of a one-way nested regional climate model (RCM) is evaluated over a region subjected to strong surface forcing: the west of North America. The sensitivity of the results to the horizontal resolution jump and updating frequency of the lateral boundary conditions are also evaluated. In order to accomplish this, a perfect-model approach nicknamed the Big-Brother Experiment (BBE) was followed. The experimental protocol consists of first establishing a virtual-reality reference climate over a fairly large area by using the Canadian RCM with grid spacing of 45 km nested within NCEP analyses. The resolution of the simulated climate is then degraded to resemble that of operational general circulation models (GCM) or observation analyses by removing small scales; the filtered fields are then used to drive the same regional model, but over a smaller sub-area. This set-up permits a comparison between two simulations of the same RCM over a common region. The Big-Brother Experiment has been carried out for four winter months over the west coast of North America. The results show that complex topography and coastline have a strong positive impact on the downscaling ability of the one-way nesting technique. These surface forcings, found to be responsible for a large part of small-scale climate features, act primarily locally and yield good climate reproducibility. Precipitation over the Rocky Mountains region is a field in which such effect is found and for which the nesting technique displays significant downscaling ability. The best downscaling ability is obtained when the ratio of spatial resolution between the nested model and the nesting fields is less than 12, and when the update frequency is more than twice a day. Decreasing the spatial resolution jump from a ratio of 12 to six has more benefits on the climate reproducibility than a reduction of spatial resolution jump from two to one. Also, it is found that an update frequency of four times a day leads to a better downscaling than twice a day when a ratio of spatial resolution of one is used. On the other hand, no improvement was found by using high-temporal resolution when the driving fields were degraded in terms of spatial resolution. Figure legends were missing in original article. Climate Dynamics (2005) 23: 473-493. The complete article is given here. DOI: 10.1007/s00382-004-0438-5     

Can added value be expected in RCM-simulated large scales?

Nested Limited-Area Models require driving data to define their lateral boundary conditions (LBC). The optimal choice of domain size and the repercussions of LBC errors on Regional Climate Model (RCM) simulations are important issues in dynamical downscaling work. The main objective of this paper is to investigate the effect of domain size, particularly on the larger scales, and to question whether an RCM, when run over very large domains, can actually improve the large scales compared to those of the driving data. This study is performed with a detailed atmospheric model in its global and regional configurations, using the “Imperfect Big-Brother” (IBB) protocol. The ERA-Interim reanalyses and five global simulations are used to drive RCM simulations for five winter seasons, on four domain sizes centred over the North American continent. Three variables are investigated: precipitation, specific humidity and zonal wind component. The results following the IBB protocol show that, when an RCM is driven by perfect LBC, its skill at reproducing the large scales decreases with increasing the domain of integration, but the errors remain small even for very large domains. On the other hand, when driven by LBC that contain errors, RCMs can bring some reduction of errors in large scales when very large domains are used. The improvement is found especially in the amplitude of patterns of both the stationary and the intra-seasonal transient components. When large errors are present in the LBC, however, these are only partly corrected by the RCM. Although results showed that an RCM can have some skill at improving imperfect large scales supplied as driving LBC, the main added value of an RCM is provided by its small scales and its skill to simulate extreme events, particularly for precipitation. Under the IBB protocol all RCM simulations were fairly skilful at reproducing small scales statistics, although the skill decreased with increasing LBC errors. Coarse-resolution model simulations have difficulties in simulating heavy precipitation events, and as a result their precipitation distributions are systematically shifted toward smaller intensity. Under the IBB protocol, all RCM simulations have distributions very similar to the reference field, being little affected by LBC errors, and no significant differences were found between the small scales statistics and the precipitation distributions obtained over different RCM domains.     

Impact of snow initialization on sub-seasonal forecasts

The influence of the snowpack on wintertime atmospheric teleconnections has received renewed attention in recent years, partially for its potential impact on seasonal predictability. Many observational and model studies have indicated that the autumn Eurasian snow cover in particular, influences circulation patterns over the North Pacific and North Atlantic. We have performed a suite of coupled atmosphere-ocean simulations with the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecast system to investigate the impact of accurate snow initialisation. Pairs of 2-month ensemble forecasts were started every 15 days from the 15th of October through the 1st of December in the years 2004–2009, with either realistic initialization of snow variables based on re-analyses, or else with “scrambled” snow initial conditions from an alternate autumn date and year. Initially, in the first 15 days, the presence of a thicker snowpack cools surface temperature over the continental land masses of Eurasia and North America. At a longer lead of 30-day, it causes a warming over the Arctic and the high latitudes of Eurasia due to an intensification and westward expansion of the Siberian High. It also causes a cooling over the mid-latitudes of Eurasia, and lowers sea level pressures over the Arctic. This “warm Arctic—cold continent” difference means that the forecasts of near-surface temperature with the more realistic snow initialization are in closer agreement with re-analyses, reducing a cold model bias over the Arctic and a warm model bias over mid-latitudes. The impact of realistic snow initialization upon the forecast skill in snow depth and near-surface temperature is estimated for various lead times. Following a modest skill improvement in the first 15 days over snow-covered land, we also find a forecast skill improvement up to the 30-day lead time over parts of the Arctic and the Northern Pacific, which can be attributed to the realistic snow initialization over the land masses.     

Tropical influence on boreal summer mid-latitude stationary waves

While organized tropical convection is a well-known source of extratropical planetary waves, state-of-the-art climate models still show serious deficiencies in simulating accurately the atmospheric response to tropical sea surface temperature (SST) anomalies and the associated teleconnections. In the present study, the remote influence of the tropical atmospheric circulation is evaluated in ensembles of global boreal summer simulations in which the Arpege-Climat atmospheric General Circulation Model (GCM) is nudged towards 6-h reanalyses. The nudging is applied either in the whole tropical band or in a regional summer monsoon domain. Sensitivity tests to the experimental design are first conducted using prescribed climatological SST. They show that the tropical relaxation does not improve the zonal mean extratropical climatology but does lead to a significantly improved representation of the mid-latitude stationary waves in both hemispheres. Low-pass filtering of the relaxation fields has no major effect on the model response, suggesting that high-frequency tropical variability is not responsible for extratropical biases. Dividing the nudging strength by a factor 10 only decreases the magnitude of the response. Model errors in each monsoon domain contribute to deficiencies in the model??s mid-latitude climatology, although an exaggerated large-scale subsidence in the central equatorial Pacific appears as the main source of errors for the representation of stationary waves in the Arpege-Climat model. Case studies are then conducted using either climatological or observed SST. The focus is first on summer 2003 characterized by a strong and persistent anticyclonic anomaly over western Europe. This pattern is more realistic in nudging experiments than in simulations only driven by observed SST, especially when the nudging domain is centred over Central America. Other case studies also show a significant tropical forcing of the summer mid-latitude stationary waves and suggest a weak influence of prescribed observed SST in the northern extratropics. Results therefore indicate that improving the tropical divergent circulation and its response to tropical SST anomalies remains a key issue for increasing the skill of extratropical seasonal predictions, not only in the winter hemisphere but also in the boreal summer hemisphere where the prediction of heatwave and drought likelihood is expected to become an important challenge with increasing concentrations of greenhouse gases.     

A study on large-scale nudging effects in regional climate model simulation

The large-scale nudging effects on the East Asian summer monsoon (EASM) are examined using the National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM). The NCEP/DOE reanalysis data is used to provide large-scale forcings for RSM simulations, configured with an approximately 50-km grid over East Asia, centered on the Korean peninsula. The RSM with a variant of spectral nudging, that is, the scale selective bias correction (SSBC), is forced by perfect boundary conditions during the summers (June–July–August) from 1979 to 2004. The two summers of 2000 and 2004 are investigated to demonstrate the impact of SSBC on precipitation in detail. It is found that the effect of SSBC on the simulated seasonal precipitation is in general neutral without a discernible advantage. Although errors in large-scale circulation for both 2000 and 2004 are reduced by using the SSBC method, the impact on simulated precipitation is found to be negative in 2000 and positive in 2004 summers. One possible reason for a different effect is that precipitation in the summer of 2004 is characterized by a strong baroclinicity, while precipitation in 2000 is caused by thermodynamic instability. The reduction of convective rainfall over the oceans by the application of the SSBC method seems to play an important role in modeled atmosphere.     

The use of successive dynamic initialization by nudging to simulate cyclogenesis during GALE IOP 1

Summary Synoptic/diagnostic case studies have increasingly come to rely on numerical simulations started from some initial state after which the model generated fields receive no further information from observed data. The purpose of this paper is to demonstrate the use of a dynamic data assimilation technique based on nudging to create a dynamically consistent high-resolution four-dimensional data set that can be used for synoptic diagnostic studies. The nudging technique is applied in the Goddard Mesoscale Atmospheric Simulation System (GMASS) using the 3-h radiosonde data collected during GALE IOP 1. A unique aspect of this application is nudging toward data analyses for which the areal coverage shifts with time. One of the two nudging simulations assimilates surface pressure in addition to the temperature, mixing ratio, and wind components. The nudging values are determined by linear interpolation between 3-h observation times. Assuming a linear variation of the assimilated value in time leads to estimates of the nudging coefficients which take into account the accuracy of the observations.Both nudging simulations are more accurate in terms of S hand root-mean-square error (RMSE) scores than a control sunulation without successive initialization. The nudging simulation with surface pressure is more accurate than the nudging simulation without surface pressure assimilation for this case. The simulation with surface pressure nudging captures the surface cyclogenesis and the associated strong rise-fall couplet in the 500 hPa height field. It also exhibits the strongest ageostrophic flow and exit region vertical circulation associated with a jet streak on the western side of the intensifying upper-level trough.The data sets made possible by the dynamic assimilation/ simulation cycles are dynamically consistent, have high spatial and temporal resolution and are ideally suited for diagnostic studies. Examples presented include the evolution of the ageostrophic flow associated with the exit region of an upper-level jet propagating toward the base of an intensifying trough with increasing cyclonic curvature of the flow. The nudging simulation with surface pressure provides the resolution and accuracy required to depict the rapid transformation (within a 12-h period) of the exit region ageostrophic flow from predominantly cross contour to along contour as the jet streak approaches the base of the trough.With 19 Figures