Atmospheric Initial Conditions Research Articles

Inclusion of potential vorticity uncertainties into a hydrometeorological forecasting chain: application to a medium size basin of Mediterranean Spain

Abstract. The improvement of the short- and mid-range numerical runoff forecasts over the flood-prone Spanish Mediterranean area is a challenging issue. This work analyses four intense precipitation events which produced floods of different magnitude over the Llobregat river basin, a medium size catchment located in Catalonia, north-eastern Spain. One of them was a devasting flash flood – known as the "Montserrat" event – which produced 5 fatalities and material losses estimated at about 65 million euros. The characterization of the Llobregat basin's hydrological response to these floods is first assessed by using rain-gauge data and the Hydrologic Engineering Center's Hydrological Modeling System (HEC-HMS) runoff model. In second place, the non-hydrostatic fifth-generation Pennsylvania State University/NCAR mesoscale model (MM5) is nested within the ECMWF large-scale forecast fields in a set of 54 h period simulations to provide quantitative precipitation forecasts (QPFs) for each hydrometeorological episode. The hydrological model is forced with these QPFs to evaluate the reliability of the resulting discharge forecasts, while an ensemble prediction system (EPS) based on perturbed atmospheric initial and boundary conditions has been designed to test the value of a probabilistic strategy versus the previous deterministic approach. Specifically, a Potential Vorticity (PV) Inversion technique has been used to perturb the MM5 model initial and boundary states (i.e. ECMWF forecast fields). For that purpose, a PV error climatology has been previously derived in order to introduce realistic PV perturbations in the EPS. Results show the benefits of using a probabilistic approach in those cases where the deterministic QPF presents significant deficiencies over the Llobregat river basin in terms of the rainfall amounts, timing and localization. These deficiences in precipitation fields have a major impact on flood forecasts. Our ensemble strategy has been found useful to reduce the biases at different hydrometric sections along the watershed. Therefore, in an operational context, the devised methodology could be useful to expand the lead times associated with the prediction of similar future floods, helping to alleviate their possible hazardous consequences.

Climate trends in a seasonal forecasting system

Deterministic forecasts produced with the second Historical Forecasting Project (HFP2) multi‐model two‐tier seasonal forecasting system are analyzed in order to assess the presence and importance of long‐term trends on seasonal forecast skill. The results show that trends in land surface temperature, 850 hPa temperature and 500 hPa geopotential height in the National Centers for Environmental Prediction (NCEP) reanalysis data, with which the forecasts are initialized and verified, are much stronger than those in the forecasts. Since biases are removed from the forecast variables, this lack of trend is unlikely to be a consequence of model climate drift. Although the atmospheric initial conditions and oceanic boundary conditions for the forecasts contain greenhouse gas and aerosol forcing information, these forcing mechanisms are not explicitly represented in the atmospheric general circulation models with which the forecasts are made. It is possible, therefore, that long‐term externally forced trends are weak or absent in the forecasts, especially over land, for this reason. The largest trends are in the December to February (DJF) season over land; their near absence in the forecasts can be at least partially overcome, and cross‐validated forecast skill increased in some regions, by correcting the forecast trends a posteriori.

Orographic triggering of long lived convection in three dimensions

A significant fraction of the occurrences of intense flash floods is due to quasi–stationary or long-lived convection that may insist on the same place for many hours, producing high values of accumulated precipitation. One of the elements that favour the initiation and anchoring of the convective system (MCS) is the orography. In one of the most severe floods (Gard basin in southern France, 8–9 September 2002), the orography of the Massif Central played a rather unusual role, favouring the onset and maintenance of the MCS at some distance upstream of the main orographic slope. In the present work the initial atmospheric conditions of this event have been largely idealized, taking horizontally uniform values for wind, temperature and humidity profiles, and a simplified isolated orography representing the sole Massif Central. A convective system is initiated in the non-hydrostatic simulations, embedded in a quasi–stationary solution of flow over the orography. It is shown that the triggering of convection occurs in the convergence zone immediately upstream of the orographic obstacle, at an altitude comparable with the mountain height. The subsequent growth of the mesoscale convective system is associated with a slow eastward drift, with the intense precipitation located upstream of the mountain and with the formation of a gust front that propagates against the incoming basic flow. Sensitivity experiments show that the development of convection critically depends on mountain height and moisture content. Although the results obtained in such idealized conditions do not reflect all the observed characteristics of the real event, they contribute to clarify the role of the orography in triggering and maintaining strong convection.

Atlantic Basin Seasonal Hurricane Simulations

Abstract An ensemble of seasonal Atlantic hurricane simulations is conducted using The Florida State University/Center for Ocean–Atmospheric Prediction Studies (FSU–COAPS) global spectral model (Cocke and LaRow) at a resolution of T126L27 (a Gaussian grid spacing of 0.94°). Four integrations comprising the ensembles were generated using the European Centre for Medium-Range Weather Forecasts (ECMWF) time-lagged initial atmospheric conditions centered on 1 June for the 20 yr from 1986 to 2005. The sea surface temperatures (SSTs) were updated weekly using the Reynolds et al. observed data. An objective-tracking algorithm obtained from the ECMWF and modified for this model’s resolution was used to detect and track the storms. It was found that the model’s composite storm structure and track lengths are realistic. In addition, the 20-yr interannual variability was well simulated by the ensembles with a 0.78 ensemble mean rank correlation. The ensembles tend to overestimate (underestimate) the numbers of storms during July (September) and produced only one CAT4–level storm on the Saffir–Simpson scale. Similar problems are noted in other global model simulations. All ensembles did well in simulating the large number of storms forming in the Atlantic basin during 1995 and showed an increase in the number of storms during La Niña and a decrease during El Niño events. The results are found to be sensitive to the choices of convection schemes and diffusion coefficients. The overall conclusion is that models such as the one used here are needed to better hindcast the interannual variability; however, going to an even higher resolution does not guarantee better interannual variability, tracks, or intensity. Improved physical parameterizations, such as using an explicit convection scheme and better representation of surface roughness at high wind speeds, are likely to more accurately represent hurricane intensity.

Factorial method as a tool for estimating the relative contribution to precipitation of cloud microphysical processes and environmental conditions: Method and application

The factorial experiment method is presented as a tool for evaluating the sensitivity of precipitation to changes in atmospheric thermodynamic conditions and cloud microphysical characteristics in mixed phase convective clouds. As an example, the method is adopted here for analysis of the sensitivity of precipitation to changes in various parameters using simulations with the Tel Aviv University 2D (TAU 2D) cloud model. The cloud model was run in 80 scenarios in order to determine the relative sensitivity of precipitation in eastern Mediterranean winter mixed phase convective clouds to changes in cloud condensation nuclei (CCN) concentration, parameterization of ice formation mechanisms and initial atmospheric thermodynamic conditions. When these parameters act synergistically to suppress precipitation (by increasing the concentration of CCN and of small ice crystals and reducing the water vapor mixing ratio), for the cases simulated here, a decrease of about 2°C in the entire profile of the ambient temperature while keeping the relative humidity constant has the same contribution to precipitation suppression as an increase of about 400 cm−3 in the CCN concentration. It is shown that under the warmest and most humid atmospheric thermodynamic conditions simulated here, the suppression of precipitation is affected more by the increased CCN than by the ice generation processes, whereas in colder atmospheric conditions the reverse is true. For a given atmospheric sounding the time of rain initiation strongly depends on the CCN concentrations. The role of ice generation begins to affect precipitation at a later stage when the cloud is more mature.

Limits, Variability, and General Behavior of Statistical Predictability of the Midlatitude Atmosphere

AbstractThe nature of statistical predictability is analyzed in a T42 global atmospheric model that is able to adequately capture the main features of the midlatitude atmosphere. Key novel features of the present study include very large prediction ensembles and information theoretic techniques. It is found globally that predictability declines in a quasi-linear fashion with time for short-term predictions (3–25 days), while for long ranges (30–45 days) there is an exponential tail. In general, beyond 45 days the prediction and climatological ensembles have essentially converged, which means that beyond that point, atmospheric initial conditions are irrelevant to atmospheric statistical prediction.Regional predictions show considerable variation in behavior. Both of the (northern) winter storm-track regions show a close-to-quasi-linear decline in predictability toward a cutoff at around 40 days. The (southern) summer storm track shows a much more exponential and considerably slower decline with a small amount of predictability still in evidence even at 90 days. Because the winter storm tracks dominate global variance the behavior of their predictability tends to dominate the global measure, except at longer lags. Variability in predictability with respect to initial conditions is also examined, and it is found that this is related more strongly to ensemble signal rather than ensemble spread. This result may serve to explain why the relation between weather forecast skill and ensemble spread is often observed to be significantly less than perfect. Results herein suggest that the ensemble signal as well as spread variations may be a major contributor to skill variations. Finally, it is found that the sensitivity of the calculated global predictability to changes in model horizontal resolution is not large; results from a T85 resolution model are not qualitatively all that different from the T42 case.

Retrieval of Atmospheric Integrated Water Vapor and Cloud Liquid Water Content Over the Ocean From Satellite Data Using the 1-D-Var Ice Cloud Microphysics Data Assimilation System (IMDAS)

Reliable prediction of precipitation by numerical weather prediction (NWP) models depends on the appropriate representation of cloud microphysical processes and accurate initial conditions of observations of atmospheric variables. Therefore, to retrieve reasonable cloud distributions, a 1-D variational Ice Cloud Microphysics Data Assimilation System (IMDAS) is developed to improve the predictability of NWP models. The general framework of IMDAS includes the Lin ice cloud microphysics scheme as a model operator, a four-stream fast microwave radiative transfer model in the atmosphere as an observation operator, and a global minimization method that is known as the shuffled complex evolution. IMDAS assimilates the satellite microwave radiometer data set of the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and retrieves integrated water vapor and integrated cloud liquid water content. This new method successfully introduces heterogeneity into the initial state of the atmosphere, and the modeled microwave brightness temperatures agree well with the observations of the Wakasa Bay Experiment 2003 in Japan. It has significantly improved the performance of the cloud microphysics scheme by the intrusion of heterogeneity into the external global reanalysis data, which resultantly improved atmospheric initial conditions.

A Comparison of Forecast Errors in CAM2 and CAM3 at the ARM Southern Great Plains Site

Abstract The authors compare short forecast errors and the balance of terms in the moisture and temperature prediction equations that lead to those errors for the Community Atmosphere Model versions 2 and 3 (CAM2 and CAM3, respectively) at T42 truncation. The comparisons are made for an individual model column from global model forecasts at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains site for the April 1997 and June–July 1997 intensive observing periods. The goal is to provide insight into parameterization errors in the CAM, which ultimately should lead to improvements in the way processes are modeled. The atmospheric initial conditions are obtained from the 40-yr ECMWF Re-Analysis (ERA-40). The land initial conditions are spun up to be consistent with those analyses. The differences between the model formulations that are responsible for the major differences in the forecast errors and/or parameterization behaviors are identified. A sequence of experiments is performed, accumulating the changes from CAM3 back toward CAM2 to demonstrate the effect of the differences in formulations. In June–July 1997 the CAM3 temperature and moisture forecast errors were larger than those of CAM2. The terms identified as being responsible for the differences are 1) the convective time scale assumed for the Zhang–McFarlane deep convection, 2) the energy associated with the conversion between water and ice of the rain associated with the Zhang–McFarlane convection parameterization, and 3) the dependence of the rainfall evaporation on cloud fraction. In April 1997 the CAM2 and CAM3 temperature and moisture forecast errors are very similar, but different tendencies arising from modifications to one parameterization component are compensated by responding changes in another component to yield the same total moisture tendency. The addition of detrainment of water in CAM3 by the Hack shallow convection to the prognostic cloud water scheme is balanced by a responding difference in the advective tendency. A halving of the time scale assumed for the Hack shallow convection was compensated by a responding change in the prognostic cloud water. Changes to the cloud fraction parameterization affect the radiative heating, which in turn modifies the stability of the atmospheric column and affects the convection. The resulting changes in convection tendency are balanced by responding changes in the prognostic cloud water parameterization tendency.

An Overview of the "Triangle Method" for Estimating Surface Evapotranspiration and Soil Moisture from Satellite Imagery

An overview of the ‘triangle’ method for estimating soil surface wetness and evapotranspiration fraction from satellite imagery is presented here. The method is insensitive to initial atmospheric and surface conditions, net radiation and atmospheric correction, yet can yield accuracies comparable to other methods. We describe the method first from the standpoint of the how the triangle is observed as obtained from aircraft and satellite image data and then show how the triangle can be created from a land surface model. By superimposing the model triangle over the observed one, pixel values from the image are determined for all points within the triangle. We further show how the stretched (or ‘universal’) triangle can be used to interpret pixel configurations within the triangle, showing how the temporal trajectories of points uniquely describe patterns of land use change. Finally, we conclude the paper with a brief assessment of the method’s limitations.

Experimental Seasonal Forecast of Monsoon 2005 Using T170L42 AGCM on PARAM Padma

As a part of the Experimental Extended Range Monsoon Prediction Experiment, ensemble mode seasonal runs for the monsoon season of 2005 were made using the National Centre for Environmental Prediction (NCEP), T170L42 AGCM. The seasonal runs were made using six initial atmospheric conditions based on the NCEP operational analysis and with forecast monthly sea-surface temperature (SST) of the NCEP Coupled forecast system (CFS). These simulations were carried out on the PARAM Padma supercomputer of Centre for Development of Advanced Computing (C-DAC), India. The model climatology was prepared by integrating the model for ten years using climatological SST as the lower boundary. The climatology of the model compares well with the observed, in terms of the spatial distribution of rainfall over the Indian land mass. The model-simulated rainfall compares well with the Tropical Rainfall Measuring Mission (TRMM) estimates for the 2005 monsoon season. Compared to the model climatology (7.81 mm/day), the model had simulated a normal rainfall (7.75 mm/day) for the year 2005 which is in agreement with the observations (99% of long-term mean). However, the model could not capture the observed increase in September rainfall from that of a low value in August 2005. The circulation patterns simulated by the model are also comparable to the observed patterns. The ensemble mean onset is found to be nearer to the observed onset date within one pentad.

Summer Rainfall Forecast Spread in an Ensemble Initialized with Different Soil Moisture Analyses

Abstract The performance of an ensemble forecasting system initialized using varied soil moisture alone has been evaluated for rainfall forecasts of six warm season convective cases. Ten different soil moisture analyses were used as initial conditions in the ensemble, which used the Weather Research and Forecasting (WRF) Advanced Research WRF (ARW) model at 4-km horizontal grid spacing with explicit rainfall. Soil moisture analyses from the suite of National Weather Service operational models—the Rapid Update Cycle, the North American Model (formerly known as the Eta Model), and the Global Forecasting System—were used to design the 10-member ensemble. For added insight, two other runs with extremely low and high soil moistures were included in this study. Although the sensitivity of simulated 24-h rainfall to soil moisture was occasionally substantial in both weakly forced and strongly forced cases, a U-shaped rank histogram indicated insufficient spread in the 10-member ensemble. This result suggests that ensemble forecast systems using soil moisture perturbations alone might not add enough variability to rainfall forecasts. Perturbations to both atmospheric initial conditions and land surface initial conditions as well as perturbations to other aspects of model physics may increase forecast spread. Correspondence ratio values for the 0.01- and 0.5-in. rainfall thresholds imply some spread in the soil moisture ensemble, but mainly in the weakly forced cases. Relative operating characteristic curves for the 10-member ensemble and for various rainfall thresholds indicate modest skill for all thresholds with the most skill associated with the lowest rainfall threshold, a result typical of warm season events.

Idealized Hot Spot Experiments with a General Circulation Model

Abstract Idealized experiments are conducted using a GCM coupled to a 20-m slab ocean model to examine the short-term response to an initial localized positive equatorial SST anomaly, or “hot spot.” A hot spot is imposed upon an aquaplanet with globally uniform 28°C SST, insolation, and trace gas concentrations designed to mimic tropical warm pool conditions. No boundary condition or external parameter other than the Coriolis parameter varies with latitude. A 15-member ensemble is initiated using random atmospheric initial conditions. A 2°C equatorial warm anomaly is switched on, along with ocean coupling (day 0). Enhanced deep convection rapidly develops near the hot spot, forcing an anomalous large-scale circulation that resembles the linear response of a dry atmosphere to a localized heating, as in the Gill model. Enhanced convection, the anomalous large-scale circulation, and enhanced wind speed peak in amplitude at about day 15. Enhanced latent heat fluxes driven primarily by an increase in vector mean wind damp the anomalous heat content of the ocean near the hot spot before day 20. Between day 20 and day 50, suppressed latent heat fluxes due to suppressed synoptic eddy variance cause a warming of the remote Tropics in regions of anomalous low-level easterly flow. This wind-driven evaporative atmosphere–ocean exchange results in a 60–70-day oscillation in tropical mean oceanic heat content, accompanied by a compensating out-of-phase oscillation in vertically integrated atmospheric moist static energy. Beyond day 70 of the simulation, positive SST anomalies are found across much of the tropical belt. These slowly decay toward the 28°C background state.

An Estimation Method for Potential Storm Surge Heights Using a New Typhoon Initialization Technique

A new application of modifying initial three-dimensional typhoon meteorologica flields for a mesoscale atmospheric model is developed in this study to evaluate potential storm surge heights that are likely to occur under expected global warming. The initialization technique based on the potential vorticity invertibility principal can arbitrarily control the position and intensity of typhoon in the initial atmospheric conditions, consistent with the balance wind and hydrostatic equilibrium. Preliminary numerical experiments of storm surge scenarios in Ise Bay, using a coupled atmosphere-ocean-wave model, show that the subtle difference of typhoon track causes a large impact on the spatial distribution of storm surge level.

A STUDY OF POTENTIAL PREDICTABILITY OF SEASONAL PREDICTION OF RIVER DISCHARGES BASED ON A DYNAMICAL ENSEMBLE METHOD

Potential predictability of seasonal mean river discharges is investigated based on an offline river ow model forced with outputs obtained from an SST-forced ensemble seasonal atmospheric forecast using a global climate model. The ensemble consists of six-member integrations with different atmospheric initial conditions with the same sea surface temperature. The variance ratio based on the analysis of the variance is used as an index of the potential predictability. The potential predictability of the river discharges in the selected three river basins of tropical Asian Monsoon regions is largely higher than that of the net water ux into the ground (P-E). This is responsible for river discharges that is obtained by integration of the surface and subsurface runoff along the river routing network of a given subbasin area since the integration of P-E over a given subbasin area has a minor in uence on the potential predictability.

Ten-year climatology of summer monsoon over South China and its surroundings simulated from a regional climate model

In a previous study by the authors, a regional climate model (hereafter the RCM) developed to study the summer monsoon over South China (SC) and the South China Sea (SCS) has been tested and found to be able to simulate to a large extent the precipitation over this region for the months of May and June. To examine the interannual variability of the summer monsoon here, it is necessary to establish a model climatology to serve as a comparison and to reduce or even remove any systematic model biases. This paper presents the analyses of such a 10-year climatology (1991–2000). The model was initialized on 1 April and integrated up to the end of June for the ten years. The initial atmospheric conditions and lateral boundary data used in this study are from the European Centre for Medium-range Weather Forecasts ‘40-year’ reanalyses. The RCM can reproduce well the main features of the monsoon circulation and vertical structure of the atmosphere. The RCM can simulate the intensification and northwestward displacement of the south Asian upper anticyclones from May to June, as well as the low-level moisture transport from the Bay of Bengal to SC. In the simulation, the average SCS summer monsoon onset occurs in the fourth pentad of May, which is consistent with the results from previous observational research. In addition, the RCM can reproduce the main characteristics of the onset such as the change of the low-level zonal flow from easterly to westerly as well as the rapid increase in daily precipitation. The SC and SCS precipitation anomalies have the correct sign in almost all the years. The shortcomings of the model simulation include an under-prediction of the strength of the subtropical high over the Northwest Pacific and the moisture transport from the Bay of Bengal to the Indochina Peninsula (IC) and SCS. A cold bias in surface air temperature is also observed, with the 10-year mean biases of the simulated surface air temperature over SC, SCS and IC in May and June being about −2.1 °C, −2.4 °C and −1.4 °C respectively. The 10-year mean biases of the simulated daily precipitation rate over SC, SCS and IC are about 2.0, −3.8 and 3.5 mm d−1 respectively. Copyright © 2005 Royal Meteorological Society.

Moisture and temperature balances at the Atmospheric Radiation Measurement Southern Great Plains Site in forecasts with the Community Atmosphere Model (CAM2)

We compare the balance of terms in moisture and temperature prediction equations during short forecasts by the Community Atmosphere Model (CAM2) with observed estimates at the Atmospheric Radiation Measurement (ARM) Southern Great Plains site for two intensive observing periods (IOPs). The goal is to provide insight into parameterization errors which ultimately should lead to model improvements. The atmospheric initial conditions are obtained from high‐resolution numerical weather prediction (NWP) analyses. The land initial conditions are spun up to be consistent with those analyses. Three cases are considered: (1) June/July 1997 when the atmosphere is relatively moist and surface evaporation corresponds to 90% of the precipitation with advection accounting for the remainder; (2) rainy days in April 1997 when the atmosphere is less moist and horizontal advection accounts for much of the precipitation with a small contribution from surface evaporation and the balance being derived from the water already present in the column; and (3) nonrainy days of the April 1997 when the moist process parameterizations are inactive and the planetary boundary layer (PBL) parameterization is dominant. For the first case the Zhang‐McFarlane deep convective parameterization drives the model to a wrong state. For the second the Hack shallow convective parameterization appears to be not acting deep enough. During both periods inconsistencies between CAM2 and ARM surface fluxes, land surface conditions and the net surface radiative fluxes indicate that the exchange parameterizations should be examined further. For the third case the PBL parameterization does not appear to create the correct vertical structure. In addition, the individual components of the dynamical tendency are very different between CAM2 and ARM, although the total dynamical tendency is similar in the two. Although these observations do not imply that those components are themselves wrong since they may be responding to other errors, each of these components should be examined further to determine the cause of their behaviors.

A coupled method for initializing El Nino Southern Oscillation forecasts using sea surface temperature

A simple method for initializing coupled general circulation models (CGCMs) using only sea surface temperature (SST) data is comprehensively tested in an extended set of ensemble hindcasts with the Max-Planck-Institute (MPI) climate model, MPI-OM/ECHAM5. In the scheme, initial conditions for both atmosphere and ocean are generated by running the coupled model with SST nudged strongly to observations. Air–sea interaction provides the mechanism through which SST influences the subsurface. Comparison with observations indicates that the scheme is performing well in the tropical Pacific. Results from a 500-yr control run show that the model’s El Nino Southern Oscillation (ENSO) variability is quite realistic, in terms of strength, structure and period. The hindcasts performed were six months long, initiated four times per year, consisted of nine ensemble members, and covered the period 1969–2001. The ensemble was generated by only varying atmospheric initial conditions, which were sampled from the initialization run to capture intraseasonal variability. At six-month lead, the model is able to capture all the major ENSO extremes of the period. However, because of poor sampling of ocean initial conditions and model deficiencies, the ensemble-mean anomaly correlation skill for Nino3 SST is only 0.6 at six-month lead. None the less, the results presented here demonstrate the potential of such a simple scheme, and provide a simple method by which SST information may be better used in more complex initialization schemes.

A Large Ensemble Analysis of the Influence of Tropical SSTs on Seasonal Atmospheric Variability

Abstract Based on a 40-member ensemble for the January–March (JFM) seasonal mean for the 1980–2000 period using an atmospheric general circulation model (AGCM), interannual variability in the first and second moments of probability density function (PDF) of atmospheric seasonal means with sea surface temperatures (SSTs) is analyzed. Based on the strength of the SST anomaly in the Niño-3.4 index region, the years between 1980 and 2000 were additionally categorized into five separate bins extending from strong cold to strong warm El Niño events. This procedure further enhances the size of the ensemble for each SST category. All the AGCM simulations were forced with the observed SSTs, and different ensemble members for specified SST boundary forcing were initiated from different atmospheric initial conditions. The main focus of this analysis is on the changes in the seasonal mean and the internal variability of tropical rainfall and extratropical 200-mb heights with SSTs. For the tropical rainfall, results indicate that in the equatorial tropical Pacific, internal variability of the tropical rainfall anomaly decreases (increases) for the La Niña (El Niño) events. On the other hand, seasonal mean variability of extratropical 200-mb height decreases for the El Niño events. Although there is increase in the seasonal mean variability of 200-mb heights for the La Niña events, results are rather inclusive. Analysis also indicates that for the variables studied, the influence of the interannual variability in SSTs is much stronger on the first moment of seasonal means compared to their influence on the internal variability. As a consequence, seasonal predictability due to changes in SSTs can be attributed primarily to the shift in the PDFs of the seasonal atmospheric means and less to changes in their spread. Modes of internal variability for 200-mb extratropical seasonal mean heights for different SST categories are also analyzed. The dominant mode of internal variability has little dependence on the tropical SST forcing, while larger influence on the second mode of internal variability is found. For SST forcing changing from a La Niña to El Niño state, the spatial pattern of the second mode shifts eastward. For the cold events, the spatial patterns bear more resemblance to the Pacific–North American (PNA) pattern, while for the warm events, it more resembles the tropical–North Hemispheric (TNH) pattern. Change in the spatial pattern of this mode from strong cold to a strong warm event resembles the change in the spatial pattern of response in the mean state.

Fast Spherical Harmonic Transform Routine FLTSS Applied to the Shallow Water Test Set

Abstract The fast spherical harmonic transform algorithm proposed by Suda and Takami is evaluated in the solutions of the shallow water equation test set defined by Williamson et al. through replacing the Legendre transforms of the NCAR spectral transform shallow water model (STSWM) with routines of the fast Legendre transform with stable sampling (FLTSS), which is the first implementation of the Suda–Takami algorithm. The Suda–Takami algorithm is an approximate algorithm with the computational complexity O(T 2 log T log ɛ−1), with T being the maximum wavenumber and ɛ the accuracy parameter of the FLTSS. The influence of the approximation errors of the FLTSS upon the numerical solutions is investigated. For all test cases of the Williamson et al. test set, the FLTSS stably solved the equations with the results that can be explained well with the accuracy ɛ. The stability in longer time integrations is also assessed, where test case 7 of an analyzed atmospheric initial condition was stably integrated for 1 yr. The FLTSS was faster than the STSWM at T170 and had higher resolutions on an Intel Mobile Pentium 4, where the lower space complexity (memory requirements) of the FLTSS was advantageous in addition to the lower computational complexity.

A coupled method for initializing El Niño Southern Oscillation forecasts using sea surface temperature

A simple method for initializing coupled general circulation models (CGCMs) using only sea surface temperature (SST) data is comprehensively tested in an extended set of ensemble hindcasts with the Max-Planck-Institute (MPI) climate model, MPI-OM/ECHAM5. In the scheme, initial conditions for both atmosphere and ocean are generated by running the coupled model with SST nudged strongly to observations. Air’sea interaction provides the mechanism through which SST influences the subsurface. Comparison with observations indicates that the scheme is performing well in the tropical Pacific.Results from a 500-yr control run show that the model’s El Niño Southern Oscillation (ENSO) variability is quite realistic, in terms of strength, structure and period. The hindcasts performed were six months long, initiated four times per year, consisted of nine ensemble members, and covered the period 1969–2001. The ensemble was generated by only varying atmospheric initial conditions, which were sampled from the initialization run to capture intraseasonal variability. At six-month lead, the model is able to capture all the major ENSO extremes of the period. However, because of poor sampling of ocean initial conditions and model deficiencies, the ensemble-mean anomaly correlation skill for Niño3 SST is only 0.6 at six-month lead. None the less, the results presented here demonstrate the potential of such a simple scheme, and provide a simple method by which SST information may be better used in more complex initialization schemes.