Atmospheric Initial Conditions Research Articles

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.

Potential Predictability in the NCEP CPC Dynamical Seasonal Forecast System

Abstract Monthly and seasonal predictions of mean atmospheric states have traditionally been viewed as a boundary forcing problem, with little regard for the role of atmospheric initial conditions (IC). The potential predictability of these mean states is investigated using hindcasted monthly mean January (JAN) and seasonal mean January– February–March (JFM) 200-hPa geopotential heights from the National Centers for Environmental Prediction Climate Prediction Center (NCEP CPC) Dynamical Seasonal Prediction System along with the corresponding data from the NCEP–National Center for Atmospheric Research (NCAR) reanalysis for the period 1980–2000. With lead times ranging from 1 to 4 months, analyses of variance tests are employed to separate the total variability into an unpredictable internal component, due to atmospheric dynamics, and a potentially predictable external component, due to the boundary forcing. These components represent the noise and signal, respectively, and areas where the signal exceeds th...

Recent advances in dynamical extra-seasonal to annual climate prediction at IAP/CAS

Recent advances in dynamical climate prediction at the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP/CAS) during the last five years have been briefly described in this paper. Firstly, the second generation of the IAP dynamical climate prediction system (IAP DCP-II) has been described, and two sets of hindcast experiments of the summer rainfall anomalies over China for the periods of 1980–1994 with different versions of the IAP AGCM have been conducted. The comparison results show that the predictive skill of summer rainfall anomalies over China is improved with the improved IAP AGCM in which the surface albedo parameterization is modified. Furthermore, IAP DCP-II has been applied to the real-time prediction of summer rainfall anomalies over China since 1998, and the verification results show that IAP DCP-II can quite well capture the large scale patterns of the summer flood/drought situations over China during the last five years (1998–2002). Meanwhile, an investigation has demonstrated the importance of the atmospheric initial conditions on the seasonal climate prediction, along with studies on the influences from surface boundary conditions (e.g., land surface characteristics, sea surface temperature). Certain conclusions have been reached, such as, the initial atmospheric anomalies in spring may play an important role in the summer climate anomalies, and soil moisture anomalies in spring can also have a significant impact on the summer climate anomalies over East Asia. Finally, several practical techniques (e.g., ensemble technique, correction method, etc.), which lead to the increase of the prediction skill for summer rainfall anomalies over China, have also been illustrated. The paper concludes with a list of critical requirements needed for the further improvement of dynamical seasonal climate prediction.

Time–Space Distribution of Long-Range Atmospheric Predictability

The global three-dimensional structure of long-range (one month to one season) atmospheric predictability was investigated with a general circulation model. The main focus was to ascertain the role of atmospheric initial conditions for such predictability as a function of lead time and space. Four types of predictability experiments with different types of initial and boundary conditions were conducted to this end. The experiments were verified against reanalysis and model data to determine real forecast skill, as well as skill under the perfect model assumption. Spatial maps and vertical cross sections of predictability at different lead times and for the two contrasting seasons were analyzed to document the varying influence of initial and boundary conditions on predictability. It was found that the atmosphere was remarkably sensitive to initial conditions on the week 3‐6 forecast range. Particularly, the troposphere over Antarctica, the region over the tropical Indian Ocean, and the lower stratosphere were affected. It was shown that most of the initial condition memory was related to the persistent nature of the atmosphere in these regions, which in turn was linked to the major modes of atmospheric variability.

Influence of atmosphere initial conditions for precipitation predictability in an Atmospheric General Circulation Model

Influence of atmosphere initial conditions for precipitation predictability in an Atmospheric General Circulation Model

Internal variability of RCM simulations over an annual cycle

Three one-year simulations generated with the Canadian RCM (CRCM) are compared to each other in order to study internal variability in nested regional climate models and to evaluate the influence exerted by the lateral boundaries information supplied by the nesting procedure. All simulations are generated over a large domain and cover an annual cycle. The simulations use different combinations of surface and atmospheric initial conditions but all of them share the same set of time-dependent lateral boundary conditions taken from a simulation by the Canadian GCM. A first simulation is used as control, the second simulation is launched with different atmospheric and surface initial conditions (IC) and the third simulation is launched taking its surface IC from the control simulation. Comparison of the root-mean-square differences (RMSD) between each pair of simulations shows two distinct seasonal behaviours in the time series of the RMSD. In winter all simulations are almost identical to each other resulting in very low RMSD values while in summer large discrepancies develop between pairs of simulations. For water vapour related fields such as precipitation or specific humidity, these discrepancies are sometimes as large as the monthly averaged variability. However, analysis of the climate statistics shows that, although the evolution of the various summer weather systems is different, the climates of each simulation are similar.

The role of boundary and initial conditions for dynamical seasonal predictability

Abstract. The importance of initial state and boundary forcing for atmospheric predictability is explored on global to regional spatial scales and on daily to seasonal time scales. A general circulation model is used to conduct predictability experiments with different combinations of initial and boundary conditions. The experiments are verified under perfect model assumptions as well as against observational data. From initial conditions alone, there is significant instantaneous forecast skill out to 2 months. Different initial conditions show different predictability using the same kind of boundary forcing. Even on seasonal time scales, using observed atmospheric initial conditions leads to a substantial increase in overall skill, especially during periods with weak tropical forcing. The impact of boundary forcing on predictability is detectable after 10 days and leads to measurable instantaneous forecast skill at very long lead times. Over the Northern Hemisphere, it takes roughly 4 weeks for boundary conditions to reach the same effect on predictability as initial conditions. During events with strong tropical forcing, these time scales are somewhat shorter. Over the Southern Hemisphere, there is a strongly enhanced influence of initial conditions during summer. We conclude that the long term memory of initial conditions is important for seasonal forecasting.

Survival of a proto-atmosphere through the stage of giant impacts: the mechanical aspects

When a giant impact occurs, atmosphere loss may occur due to global ground motion excited by a strong shock wave traveling in the planetary interior. Here, the relations between the ground motion and the amount of the lost atmosphere are systematically investigated through calculations of a spherically one-dimensional atmospheric motion for various initial atmospheric conditions. The fraction of the lost atmosphere to the total mass of the atmosphere is found to be controlled only by the ground velocity and, insensitive to the initial atmospheric conditions. Unlike the previous studies (Ahrens, 1990, Origin of the Earth, H.E. Newson, J.H. Jones (Eds.), pp. 211–227; Ahrens, 1993, Annu. Rev. Earth Planet. Sci. 21, 525–555; Chen and Ahrens, 1997, Phys. Earth Planet. Inter. 100, 21–26); the estimated loss fraction for the giant impact is only 20%. Significant escape occurs only when the ground velocity is close to the escape velocity. Thus, most of the atmosphere should survive the giant impact. The cause of the difference from previous estimates is discussed from energetic and dynamic points of view. Moreover, if our estimates are applied to the atmosphere of the impactor planet, a significant fraction of it is carried to the target planet. Survival of the proto-atmosphere has very important effects on the origin and evolution of the terrestrial planets' volatile budget.

Forced and Free Intraseasonal Variability over the South Asian Monsoon Region Simulated by 10 AGCMs

Abstract This study examines intraseasonal (20–70 day) variability in the South Asian monsoon region during 1997/98 in ensembles of 10 simulations with 10 different atmospheric general circulation models. The 10 ensemble members for each model are forced with the same observed weekly sea surface temperature (SST) but differ from each other in that they are started from different initial atmospheric conditions. The results show considerable differences between the models in the simulated 20–70-day variability, ranging from much weaker to much stronger than the observed. A key result is that the models do produce, to varying degrees, a response to the imposed weekly SST. The forced variability tends to be largest in the Indian and western Pacific Oceans where, for some models, it accounts for more than a quarter of the 20–70-day intraseasonal variability in the upper-level velocity potential during these two years. A case study of a strong observed Madden–Julian oscillation (MJO) event shows that the models...

NCEP Dynamical Seasonal Forecast System 2000

The new National Centers for Environmental Prediction (NCEP) numerical seasonal forecast system is described in detail. The new system is aimed at a next-generation numerical seasonal prediction in which focus is placed on land processes, initial conditions, and ensemble methods, in addition to the tropical SST forcing. The atmospheric model physics is taken from the NCEP–National Center for Atmospheric Research (NCAR) reanalysis model, which has more comprehensive land hydrology and improved physical processes. The model was further upgraded by introducing three new parameterization schemes: 1) the relaxed Arakawa–Schubert (RAS) convective parameterization, which improved middle latitude response to tropical heating; 2) Chou's shortwave radiation, which corrected surface radiation fluxes; and 3) Chou's longwave radiation scheme together with smoothed mean orography that reduced model warm bias. Atmospheric initial conditions were taken from the operational NCEP Global Data Assimilation System, allowing the seasonal forecast to start from realistic initial conditions and to seamlessly connect with the short- and medium-range forecasts. The Pacific basin ocean model is the same as that in the old NCEP seasonal system and is coupled to the new atmospheric model with a two-tier approach. The operational atmospheric forecast is performed once a month with a 20-member ensemble. Prior to the forecast, 10-member ensemble hindcasts of the same month from 1979 to the present are performed to define model climatology and model forecast skill. The system has been running routinely since April 2000, and the products are available online at NWS's ftp site.

The 1998 Oklahoma–Texas Drought: Mechanistic Experiments with NCEP Global and Regional Models

Abstract This study presents results from mechanistic experiments to clarify the origin and maintenance of the Oklahoma–Texas (OK–TX) drought of the 1998 summer, using the National Centers for Environmental Prediction (NCEP) global and regional models. In association with this unprecedented drought, three major mechanisms that can produce extended atmospheric anomalies have been identified: (i) sea surface temperature (SST) anomalies, (ii) soil moisture anomalies, and (iii) atmospheric initial conditions favorable to such a climate extreme even in the absence of surface forcing (i.e., internal forcing). The authors found that the SST anomalies during April–May 1998 established the large-scale conditions for the drought. However, the warm El Nino–Southern Oscillation (ENSO) SST anomalies over the central and eastern tropical Pacific alone did not play a major role in initiating the drought. The internal structure of atmospheric conditions played as significant a role as the SST anomalies over the globe. In...

Large sensitivity to initial conditions in seasonal predictions with a coupled ocean‐atmosphere general circulation model

An ensemble of one‐year forecasts differing only in details of the atmospheric initial conditions was produced with a coupled ocean‐atmosphere general circulation model (GCM) in order to investigate the predictability of the coupled system. For some ocean initial conditions, the evolution of the tropical Pacific ocean thermal structure seems to be relatively deterministic for lead times out to one year. However, there are other ocean initial conditions, mostly in the mid 1990's for which coupled model forecasts of the tropical Pacific are much more sensitive to details of the atmosphere initial conditions. In some cases, the ensemble forecasts appear to split, with some ensemble members predicting El Niño‐like conditions, and others predicting La Niña. Very large ensembles were run for several of these cases. Very slight perturbations added to the atmospheric initial conditions led to large spread in predicted SST anomalies in some years. These are model results, however, they do suggest the possibility that seasonal predictions of the coupled tropical system may be highly non‐deterministic in some years.

A STUDY OF ACCURACY AND POTENTIAL PREDICTABILITY OF SEASONAL PREDICTION OF WATER RESOURCES BASED ON ATMOSPHERIC SEASONAL FORECAST EXPERIMENTS

Interannual variability and potential predictability of seasonal mean water resources is investigated based on the SST-forced ensemble seasonal atmospheric forecast using Japan Meteorological Agency global model. The ensemble consists of four model integrations with different atmospheric initial conditions with the same sea surface temperature. The interannual variability of water resources (residual of Precipitation to Evapration: P-E) are not simulated well especially for the rainy season over the selected four regions of Asia. Howere, the ensemble mean of the seasonal atmospheric forecast could contribute to the seasonal water resources at some regions. The potential predictability of P-E is largely low at the land areas in from mid-to high-latitude. It is lower than that of precipitation in North Africa and central Eurasia continents.

An examination of the sensitivity of numerically simulated wildfires to low-level atmospheric stability and moisture, and the consequences for the Haines Index

The Haines Index, an operational fire–weather index introduced in 1988 and based on the observed stability and moisture content of the near-surface atmosphere, has been a useful indicator of the potential for high-risk fires in low wind conditions and flat terrain. The Haines Index is of limited use, however, as a predictor of actual fire behavior. To develop a fire–weather index to predict severe or erratic wildfire behavior, an understanding of how the ambient lower-level atmospheric stability and moisture affects the growth of a wildfire is needed. This study is a first step in this process. This study investigates, through four comparative numerical simulations with a coupled wildfire–atmosphere model, the sensitivity of wildland fires to atmospheric stability and moisture, and in the process explores the correspondence between atmospheric stability and moisture, wildfire behavior, and the Haines Index. In the first three fire simulations, the model atmosphere was initially set to identical moisture but different instability conditions that correspond to Haines Indexes for low, moderate, and high potential for severe fire development. In the fourth fire simulation, the initial atmospheric and moisture conditions were for a high-risk fire Haines Index rating, but different from the initial conditions of dryness and stability of the previous experiments. The study indicates that high-risk fire development is sensitive to near-surface atmospheric stability and moisture, and that there is a range of atmospheric stability and moisture conditions that is important to the development of severe or erratic fire behavior, and that this range is within the atmospheric stability and moisture conditions represented by a Haines Index for high potential for severe fire. The analyses also suggest that there is a substantial latitude of fire behavior for fires rated as this Index, indicating that this Index should be further divided, or refined.

Recent Researches on the Short-Term Climate Prediction at IAP—A Brief Review

Studies on the seasonal to extraseasonal climate prediction at the Institute of Atmospheric Physics (IAP) in recent years were reviewed. The first short-term climate prediction experiment was carried out based on the atmospheric general circulation model (AGCM) coupled to a tropical Pacific oceanic general circulation model (OGCM). In 1997, an ENSO prediction system including an oceanic initialization scheme was set up. At the same time, researches on the SST-induced climate predictability over East Asia were made. Based on the biennial signal in the interannual climate variability, an effective method was proposed for correcting the model predicted results recently. In order to consider the impacts of the initial soil moisture anomalies, an empirical scheme was designed to compute the soil moisture by use of the atmospheric quantities like temperature, precipitation, and so on. Sets of prediction experiments were carried out to study the impacts of SST and the initial atmospheric conditions on the flood occurring over China in 1998.

Predictability of the 1997 and 1998 South Asian Summer Monsoon Low-Level Winds

The predictability of the 1997 and 1998 south Asian summer monsoon winds is examined from an ensemble of 10 atmospheric general circulation model simulations with prescribed sea surface temperatures (SSTs) and soil moisture. The simulations have no memory of atmospheric initial conditions for the periods of interest. The model simulations show that the 1998 monsoon is considerably more predictable than the 1997 monsoon. During May and June of 1998 the predictability of the low-level wind anomalies is largely associated with a local response to anomalously warm Indian Ocean SSTs. Predictability increases late in the season (July and August) as a result of the strengthening of the anomalous Walker circulation and the associated development of easterly low-level wind anomalies that extend westward across India and the Arabian Sea. During these months the model is also the most skillful, with the analyses showing a similar late-season westward extension of the easterly wind anomalies. The model shows little predictability or skill in the monthly mean low-level winds over Southeast Asia during 1997. Predictable wind anomalies do occur over the western Indian Ocean and Indonesia; however, over the Indian Ocean the predictability is artificial, because the model is responding to SST anomalies that were wind driven. The reduced predictability in the low-level winds during 1997 appears to be the result of a weaker (as compared with 1998) simulated anomalous Walker circulation, and the reduced skill is associated with pronounced intraseasonal activity that is not captured well by the model. It is remarkable that the model does produce an ensemble mean Madden–Julian oscillation (MJO) response, though it is approximately in quadrature with, and much weaker than, the observed MJO anomalies during 1997.

Simulation of Monsoon Disturbances in a GCM

—A large part of the rainfall over India during the summer monsoon season (June–September) is contributed by synoptic scale disturbances such as monsoon depressions. To study the evolution of such disturbances in Atmospheric General Circulation Models (AGCM), the Hadley Centre AGCM (HadAM2b) has been integrated for 15 summer monsoons (1979–1993) and the output was examined for the presence of synoptic scale disturbances such as monsoon depressions, low pressure areas, land lows and land depressions over the Indian summer monsoon region. The atmospheric initial condition for each of these integrations was of 23rd May and observed Sea Surface Temperatures (SST) were described as a boundary condition.¶Although the horizontal resolution of the AGCM used in this study is only 2.5°× 3.75° lat. long., the model is able to simulate a few monsoon disturbances. The important features of these simulated disturbances are presented. The features of the simulated disturbances are realistic. The morphologies of a well simulated monsoon depression and a simulated low pressure area are presented as examples. The frequency of the simulated monsoon depressions is less than the climatological frequency of the depressions during all four monsoon months.

Impact of initial conditions on seasonal simulations with an atmospheric general circulation model

AbstractMany previous studies have examined the use of very long integrations of atmospheric general circulation models (AGCMs) forced by observed sea surface temperatures (SSTs) as proxies for seasonal atmospheric predictions. These long simulations explore a boundary‐value problem in which significant deviations from the model's long‐term climatology must be a result of the SST forcing. Seasonal lead simulations starting with observed initial conditions (ICs) for the atmosphere and land surface while retaining observed SST forcing are an intermediate step between the pure boundary‐value problem and the pure initial‐value forecast problem in which SSTs are also predicted. As part of the Dynamical Seasonal Prediction (DSP) experiment, an ensemble of AGCM integrations with observed atmospheric ICs and model climatology land surface ICs was integrated from mid‐December through March for 16 years. These DSP simulation ensembles are compared to ensembles of long boundary‐value simulations from the same AGCM in a perfect‐model setting (no comparisons of simulations to observations are attempted). Significant differences must be due to the impact of the DSP ICs. Surprisingly large and long‐lived differences are found in both the mean and the variance of the ensembles. Many appear to occur because the ICs of the DSP runs are inconsistent with the AGCM climatology; an extended period of model ‘spin‐up’ is the result. Some differences are related to local impacts of the land surface ICs while others, like shifts in the distribution of tropical precipitation and a cooling of the northern hemisphere, are less obviously related to the ICs. The results suggest that care will be needed when inserting observed ICs into seasonal predictions in order to avoid the long‐term effects of model spin‐up.

Seasonal Prediction over North America with a Regional Model Nested in a Global Model

Ensembles of winter and summer seasonal hindcasts have been carried out with an 80-km resolution version of the National Centers for Environmental Prediction, Environmental Modeling Center Eta model over the North American region. The lateral boundary conditions for the Eta model are prescribed from Center for Ocean–Land–Atmosphere R40 atmospheric general circulation model integrations, which used observed atmospheric initial conditions and observed global sea surface temperature (SST). An examination of 15 seasonal winter hindcasts and 15 seasonal summer hindcasts shows that the nested model reduces the systematic errors in seasonal precipitation compared to the global model alone. The physical parameterizations, enhanced resolution, and better representation of orography in the Eta model produces better simulations of precipitation, and in some cases, its interannual variability. In particular, the precipitation difference between the 1988 drought and 1993 flood over the United States was much better simulated by the nested model. The predictions of circulation features were generally as good or better than those from the global model alone. Estimates of external (SST forced “signal”) and internal (dynamics generated “noise”) variability were made for both the global model and the nested model predictions. Contrary to the expectation that a higher-resolution model would have higher internal-dynamics-generated variability, the signal, noise, and signal-to-noise ratios of the near-surface temperature and precipitation fields were generally quite similar between the nested model and the global model predictions. In the winter season the nested model had larger signal-to-noise ratios in both temperature and precipitation than did the global model alone.