Atmospheric Initial Conditions Research Articles

Initial Transient Response of the Winter Polar Stratospheric Vortex to Idealized Equatorial Pacific Sea Surface Temperature Anomalies in the NCAR WACCM

Abstract Perpetual winter simulations using the NCAR Whole Atmosphere Community Climate Model (WACCM) are conducted to document the differences of the initial transient response of the boreal winter Northern Hemisphere stratospheric polar vortex to central (CPW) and eastern Pacific warming (EPW) events. Idealized patches of positive sea surface temperature (SST) anomalies are superimposed onto a climatological SST field to mimic canonical CPW and EPW forcings. A 20-member ensemble was created by varying initial atmospheric conditions for both CPW and EPW cases. In the ensemble average, the vortex weakens under both CPW and EPW forcing, indicated by a negative zonal mean zonal wind tendency. This tendency is mainly tied to changes in the eddy-driven mean meridional circulation (MMC). A negative anomaly in the eddy momentum flux convergence also plays a secondary role in the weakening. The vortex response, however, differs dramatically among individual ensemble members. A few ensemble members exhibit initial vortex strengthening although weaker in magnitude and shorter in duration than the initial weakening in the ensemble average. The initial state and the subsequent internal variation of the extratropical atmosphere is at least as important as the type of SST forcing in determining the transient response of the stratospheric polar vortex. Interactions between the internal variability of the vortex and SST-driven wave anomalies ultimately determine the nature of the initial transient response of the vortex to EPW and CPW forcing. This sensitivity to the initial atmospheric state has implications for understanding medium-range forecasts of the extratropical atmospheric response to emerging tropical SST anomalies, particularly over high-latitude regions.

Prediction and monitoring of monsoon intraseasonal oscillations over Indian monsoon region in an ensemble prediction system using CFSv2

An ensemble prediction system (EPS) is devised for the extended range prediction (ERP) of monsoon intraseasonal oscillations (MISO) of Indian summer monsoon (ISM) using National Centers for Environmental Prediction Climate Forecast System model version 2 at T126 horizontal resolution. The EPS is formulated by generating 11 member ensembles through the perturbation of atmospheric initial conditions. The hindcast experiments were conducted at every 5-day interval for 45 days lead time starting from 16th May to 28th September during 2001–2012. The general simulation of ISM characteristics and the ERP skill of the proposed EPS at pentad mean scale are evaluated in the present study. Though the EPS underestimates both the mean and variability of ISM rainfall, it simulates the northward propagation of MISO reasonably well. It is found that the signal-to-noise ratio of the forecasted rainfall becomes unity by about 18 days. The potential predictability error of the forecasted rainfall saturates by about 25 days. Though useful deterministic forecasts could be generated up to 2nd pentad lead, significant correlations are found even up to 4th pentad lead. The skill in predicting large-scale MISO, which is assessed by comparing the predicted and observed MISO indices, is found to be ~17 days. It is noted that the prediction skill of actual rainfall is closely related to the prediction of large-scale MISO amplitude as well as the initial conditions related to the different phases of MISO. An analysis of categorical prediction skills reveals that break is more skillfully predicted, followed by active and then normal. The categorical probability skill scores suggest that useful probabilistic forecasts could be generated even up to 4th pentad lead.

Improving Intraseasonal Prediction with a New Ensemble Generation Strategy

Abstract The Australian Bureau of Meteorology has recently enhanced its capability to make coupled model forecasts of intraseasonal climate variations. The Predictive Ocean Atmosphere Model for Australia (POAMA, version 2) seasonal prediction forecast system in operations prior to March 2013, designated P2-S, was not designed for intraseasonal forecasting and has deficiencies in this regard. Most notably, the forecasts were only initialized on the 1st and 15th of each month, and the growth of the ensemble spread in the first 30 days of the forecasts was too slow to be useful on intraseasonal time scales. These deficiencies have been addressed in a system upgrade by initializing more often and through enhancements to the ensemble generation. The new ensemble generation scheme is based on a coupled-breeding approach and produces an ensemble of perturbed atmosphere and ocean states for initializing the forecasts. This scheme impacts favorably on the forecast skill of Australian rainfall and temperature compared to P2-S and its predecessor (version 1.5). In POAMA-1.5 the ensemble was produced using time-lagged atmospheric initial conditions but with unperturbed ocean initial conditions. P2-S used an ensemble of perturbed ocean initial conditions but only a single atmospheric initial condition. The improvement in forecast performance using the coupled-breeding approach is primarily reflected in improved reliability in the first month of the forecasts, but there is also higher skill in predicting important drivers of intraseasonal climate variability, namely the Madden–Julian oscillation and southern annular mode. The results illustrate the importance of having an optimal ensemble generation strategy.

Importance of initial state and atmospheric conditions for urban land surface models’ performance

Urban land surface models (LSM) are commonly evaluated for short periods (a few weeks to months) because of limited observational data. This makes it difficult to distinguish the impact of initial conditions on model performance or to consider the response of a model to a range of possible atmospheric conditions. Drawing on results from the first urban LSM comparison, these two issues are considered. Assessment shows that the initial soil moisture has a substantial impact on the performance. Models initialised with soils that are too dry are not able to adjust their surface sensible and latent heat fluxes to realistic values until there is sufficient rainfall. Models initialised with soils too wet are not able to restrict their evaporation appropriately for periods in excess of a year. This has implications for short term evaluation studies and implies the need for soil moisture measurements to improve data assimilation and model initialisation. In contrast, initial conditions influencing the thermal storage have a much shorter adjustment timescale compared to soil moisture. Most models partition too much of the radiative energy at the surface into the sensible heat flux at the probable expense of the net storage heat flux.

Seasonal Predictability of the Southern Annular Mode due to Its Association with ENSO

Abstract Predictability of the southern annular mode (SAM) for lead times beyond 1–2 weeks has traditionally been considered to be low because the SAM is regarded as an internal mode of variability with a typical decorrelation time of about 10 days. However, the association of the SAM with El Niño–Southern Oscillation (ENSO) suggests the potential for making seasonal predictions of the SAM. In this study the authors explore seasonal predictability and the predictive skill of SAM using observations and retrospective forecasts (hindcasts) from the Australian Bureau of Meteorology dynamical seasonal forecast system [the Predictive Ocean and Atmosphere Model for Australia, version 2 (POAMA2)]. Based on the observed seasonal relationships of the SAM with tropical sea surface temperatures, two distinctive periods of high seasonal predictability are suggested: austral late autumn to winter and late spring to early summer. Predictability of the SAM in the austral cold seasons stems from the association of the SAM with warm-pool (or Modoki/central Pacific) ENSO, whereas predictability in the austral warm seasons stems from the association of the SAM with cold-tongue (or eastern Pacific) ENSO. Using seasonal hindcasts for 1980–2010 from POAMA2, it is shown that the observed relationship between SAM and ENSO is faithfully depicted and SST variations associated with ENSO are skillfully predicted. Consequently, POAMA2 can skillfully predict the phase and amplitude of seasonal anomalies of the SAM in early summer and early winter for at least one season in advance. Zero-lead monthly forecasts of the SAM are furthermore shown to be highly skillful in almost all months, which is ascribed to predictability stemming from observed atmospheric initial conditions.

ORIGIN OF MACROSPICULE AND JET IN POLAR CORONA BY A SMALL-SCALE KINKED FLUX TUBE

We report an observation of a small scale flux-tube that undergoes kinking and triggers the macrospicule and a jet on November 11, 2010 in the north polar corona. The small-scale flux-tube emerged well before the triggering of macrospicule and as the time progresses the two opposite halves of this omega shaped flux-tube bent transversely and approached towards each other. After $\sim$ 2 minutes, the two approaching halves of the kinked flux-tube touch each-other and internal reconnection as well as energy release takes place at the adjoining location and a macrospicule was launched which goes upto a height of 12 Mm. Plasma starts moving horizontally as well as vertically upward along with the onset of macrospicule and thereafter converts into a large-scale jet which goes up to $\sim$ 40 Mm in the solar atmosphere with a projected speed of $\sim$ 95 km s$^{-1}$. We perform 2-D numerical simulation by considering the VAL-C initial atmospheric conditions to understand the physical scenario of the observed macrospicule and associated jet. The simulation results show that reconnection generated velocity pulse in the lower solar atmosphere steepens into slow shock and the cool plasma is driven behind it in form of macrospicule. The horizontal surface waves also appeared with the shock fronts at different heights, which most likely drove and spread the large-scale jet associated with the macrospicule.

Assessing the performance of Intergovernmental Panel on Climate Change AR5 climate models in simulating and projecting wind speeds over China

The ability of nine current generation (Coupled Model Intercomparison Project Phase 5, CMIP‐5) coupled atmosphere‐ocean general circulation models (AOGCMs) to accurately simulate the near‐surface wind climate over China is evaluated by comparing output from the historical period (1971–2005) with an observational data set and reanalysis output. Results suggest the AOGCMs show substantial positive bias in the mean 10 m wind speed relative to observations and the ERA‐40, National Centers for Environmental Prediction–Department of Energy, and National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis. Given that the models generally produce the upper level geopotential height gradients comparatively well, it is postulated that one major reason for the discrepancy between observed and modeled wind fields is the surface characterization used in the AOGCMs. All models exhibit lower interannual variability than reanalysis data and observations, and none of the models reproduce the recent decline in wind speed that is manifest in the near‐surface observations. The wind speed of individual model runs during the historical period does not exhibit much influence from the initial atmospheric conditions. The output for the current century from seven of the AOGCMs is examined relative to the historical wind climate. The results indicate that spatial fields of wind speed at the end of the 21st century are very similar to those of the last 35 years with comparatively little response to the precise representative concentration pathway scenario applied.

The spring prediction barrier in ENSO hindcast experiments using the FGOALS-g model

The Flexible Global Ocean-Atmosphere-Land System Model-gamil (FGOALS-g) was used to study the spring prediction barrier (SPB) in an ensemble system. This coupled model was developed and maintained at the State Key Laboratory of Atmospheric Sciences and Geophysical Fluid Dynamics (LASG). There are two steps in our hindcast experiments. The first is to integrate the coupled model continuously with sea surface temperature (SST) nudging, from 1971 to 2006. The second is to carry out a series of one-year hindcasts without SST nudging, by adopting initial values from the first step on January 1 st, April 1 st, July 1 st, and October 1 st, from 1982 to 2005. We generate 10 ensemble members for a particular start date (1 st ) by choosing different atmospheric and land conditions around the hindcast start date (1 st through 10 th ). To estimate the predicted SST, two methods are used: (1) Anomaly Correlation Coefficient and its rate of decrease; and (2) Talagrand distribution and its standard deviation. Results show that FGOALS-g offers a reliable ensemble system with realistic initial atmospheric and oceanic conditions, and high anomaly correlation (>0.5) within 6 month lead time. Further, the ensemble approach is effective, in that the anomaly correlation of ensemble mean is much higher than that of most individual ensemble members. The SPB exists in the FGOALS-g ensemble system, as shown by anomaly correlation and equal likelihood. Nevertheless, the role of the ensemble mean in reducing the SPB of ENSO prediction is significant. The rate of decrease of the ensemble mean is smaller than the largest deviations by 0.04–0.14. At the same time, the ensemble system “equal likelihood” declines during spring. An ensemble mean helps give a correct prediction direction, departing from largely-deviated ensemble members.

Characterization of Concentrating Photovoltaic modules by cooperative competitive Radial Basis Function Networks

Concentrating Photovoltaic (CPV) technology attempts to optimize the efficiency of solar energy production systems. As conventional Photovoltaic (PV) technology, suffers from variability in its production and needs models for determining the exact module performance. There are several problems when analyzing CPV systems performance with traditional techniques due to absence of standardization. In this sense it is remarkable the importance for the emerging CPV technology, of the existence of models which allow the prediction of modules performance from initial atmospheric conditions. In this paper, a CPV module is studied by means of atmospheric conditions obtained using an automatic test and measuring system developed by the authors. The characterization of the CPV module is carried out considering incident normal irradiance, ambient temperature, spectral irradiance distribution and wind speed. CO2RBFN, a cooperative-competitive algorithm for the design of radial basis neural networks, is adapted and applied to these data obtaining a model with a good level of accuracy on test data, improving the results obtained by other methods considered in the experimental comparison. These results are promising and the obtained model could be used to work out the maximum power at the CPV reporting conditions and to analyze the performance of the module under any conditions and at any moment.

Evaluation of the Added Value of Regional Ensemble Forecasts on Global Ensemble Forecasts

Abstract The regional single-model-based Aire Limitée Adaptation Dynamique Développement International–Limited Area Ensemble Forecasting (ALADIN-LAEF) ensemble prediction system (EPS) is evaluated and compared with the global ECMWF-EPS to investigate the added value of regional to global EPS models. ALADIN-LAEF consists of 16 perturbed members at 18-km horizontal resolution, while ECMWF-EPS includes 50 perturbed members at 50-km horizontal resolution. In ALADIN-LAEF, the atmospheric initial condition uncertainty is quantified by using blending, which combines large-scale uncertainty generated by the ECMWF-EPS singular-vector approach with small-scale perturbations resolved by the ALADIN breeding technique. The surface initial condition perturbations are generated by use of the noncycling surface breeding (NCSB) technique, and different physics schemes are employed for different forecast members to account for model uncertainties. The verification and comparison have been carried out for a 2-month period during summer 2007 over central Europe. The results show a quite favorable level of performance for ALADIN-LAEF compared to ECMWF-EPS for surface weather variables. ALADIN-LAEF adds more value to precipitation forecasts and has greater skill for 10-m wind and mean sea level pressure results than does ECMWF-EPS. For 2-m temperature, ALADIN-LAEF forecasts have larger spread, are statistically more consistent, but also have less skill than ECMWF-EPS due to the strong cold bias in the ALADIN forecasts. For the upper-air weather parameters, the forecast of ALADIN-LAEF has a larger spread, but the forecast skill of ALADIN-LAEF is from neutral to slightly inferior compared to ECMWF-EPS. It may be concluded that a regional single-model-based EPS with fewer ensemble members could provide more added value in terms of greater skill for near-surface weather variables than the global EPS with larger ensemble size, whereas it may have limitations when applied to upper-air weather variables.

Sensitivity of 0–12-h Warm-Season Precipitation Forecasts over the Central United States to Model Initialization

Abstract Sensitivity of 0–12-h warm-season precipitation forecasts to atmospheric initial conditions, including those from different large-scale model analyses and from rapid cycled (RC) three-dimensional variational data assimilations (3DVAR) with and without radar data, is investigated for a 6-day period during the International H2O Project. Neighborhood-based precipitation verification is used to compare forecasts made with the Advanced Research core of the Weather Research and Forecasting Model (ARW-WRF). Three significant convective episodes are examined by comparing the precipitation patterns and locations from different forecast experiments. From two of these three case studies, causes for the success and failure of the RC data assimilation in improving forecast skill are shown. Results indicate that the use of higher-resolution analysis in the initialization, rapid update cycling via WRF 3DVAR data assimilation, and the additional assimilation of radar observations each play a role in shortening the period of the initial precipitation spinup as well as in placing storms closer to observations, thus improving precipitation forecast skill by up to 8–9 h. Impacts of data assimilation differ for forecasts initialized at 0000 and 1200 UTC. The case studies show that the pattern and location of the forecasted precipitation were noticeably improved with radar data assimilation for the two late afternoon cases that featured lines of convection driven by surface-based cold pools. In contrast, the RC 3DVAR, both with and without radar data, had negative impacts on convective forecasts for a case of morning elevated convection associated with a midlatitude short-wave trough.

Impacts of Model Resolutions and Initial Conditions on Predictions of the Asian Summer Monsoon by the NCEP Climate Forecast System

Abstract A series of 60-day hindcasts by the Climate Forecast System (CFS) of the National Centers for Environmental Prediction is analyzed to understand the impacts of atmospheric model resolutions and initial conditions on predictions of the Asian summer monsoon. The experiments, for the time period 2002–06 and with 14 ensemble members, are conducted at resolutions of T62, T126, and T254. They are initialized every 5 days from May to August, using the operational global atmospheric data assimilation system and operational global ocean data assimilation. It is found that, in predicting the magnitude and the timing of monsoon rainfall over lands, high model resolutions overall perform better than lower model resolutions. The increase in prediction skills with model resolution is more apparent over South Asia than over Southeast Asia. The largest improvement is seen over the Tibetan Plateau, at least for precipitation. However, the increase in model resolution does not enhance the skill of the predictions over oceans. Overall, model resolution has larger impacts than do the initial conditions on predicting the development of the Asian summer monsoon in the early season. However, higher model resolutions such as T382 may be needed for the CFS to simulate and predict many features of the monsoon more realistically.

On the Sensitivity of Short-Term Climate Prediction to Initial Atmospheric Conditions

Two different sets of ensemble hindcasting experiments were performed with a grid-point Atmospheric General Circulation Model (IAP9L2x2.5-AGCM) forced by actual observed and climatic surface sea temperature(SST), sea ice respectively for seventeen years(1988-2004).In each of set of experiment, there were three tests from three different initial atmospheric conditions . Then the sensitivity of short-term climate prediction to initial atmospheric conditions was investigated. A simple correlation analysis showed that the influence of initial atmospheric conditions in the tropics is greater than that in the extratropics , so initial atmospheric conditions play a very important role on short-term climate prediction, especially in China. In addition, the initial atmospheric conditions of smoothing 5-day NCEP-DOE reanalysis2 data were the best from most of meteorological variables in this study.

Climate change projections over South America in the late 21st century with the 20 and 60 km mesh Meteorological Research Institute atmospheric general circulation model (MRI-AGCM)

[1] Two 25 year time-slice experiments were conducted using a 20 km mesh global atmospheric model, one for the present (1979–2003) and the other for the future (2075–2099). To assess the uncertainty of climate change projections, we performed ensemble simulations with the 60 km mesh model combining 4 different sea surface temperatures and 3 atmospheric initial conditions. Horizontal resolution of these global models is higher than or comparable to that of regional climate models applied to South American climate change projections. Both the 20 km mesh model and 60 km mesh model reproduce sufficiently well the observed seasonal precipitation patterns. These models project an increase in wet-season precipitation and a decrease in dry-season precipitation over most of South America. In the future, almost all over South America, precipitation intensity will increase. In particular, precipitation intensity is largest over the southeast South America in the present-day simulation, where future change is also large, implying an increasing risk of flooding in this region including the Parana River. At the same time a large increase of consecutive dry days is projected over the western part of the Amazon, where the amplitude of the seasonal hydrograph is projected to increase in the Amazon River, implying more floods in wet season and droughts in dry season.

Future changes in the East Asian rain band projected by global atmospheric models with 20-km and 60-km grid size

Global warming projection experiments were conducted using a 20-km mesh global atmospheric model, focusing on the change in the rain band of East Asian summer monsoon. To assess the uncertainty of climate change projections, we performed ensemble simulations with the 60-km resolution model combining four different SSTs and three atmospheric initial conditions. In the present-day climate simulations, the 20-km model reproduces the rain band of East Asian summer monsoon better than lower resolution models in terms of geographical distribution and seasonal march. In the future climate simulation by the 20-km model, precipitation increases over the Yangtze River valley in May through July, Korean peninsula in May, and Japan in July. The termination of rainy season over Japan tends to be delayed until August. Ensemble simulations by the 60-km model show that precipitation in the future climate for July increases over the Yangtze River valley, the East China Sea and Japan. These changes in precipitation are partly consistent with those projected by the 20-km model. Simulations by the 20-km and 60-km models consistently show that in the future climate the termination of rainy season over Japan tends to be delayed until August. The changes in the vertically integrated water vapor flux show the intensification of clockwise moisture transport over the western Pacific subtropical high. Most precipitation changes over the East Asia can be interpreted as the moisture convergence resulting from change in the horizontal transport of water vapor.

The Central European limited‐area ensemble forecasting system: ALADIN‐LAEF

AbstractThe Central European limited‐area ensemble forecasting system ALADIN‐LAEF (Aire Limitée Adaptation Dynamique Développement InterNational—Limited‐Area Ensemble Forecasting) has been developed within the framework of ALADIN international cooperation and the Regional Cooperation for Limited‐Area modelling in Central Europe (RC LACE). It was put into pre‐operation in March 2007. The main feature of the pre‐operational ALADIN‐LAEF was the dynamical downscaling of the global ensemble forecast from the European Centre for Medium‐range Weather Forecasts (ECMWF). In 2009, ALADIN‐LAEF was upgraded with several methods for dealing with the forecast uncertainties to improve the forecast quality. These are: (1) the blending method, which combines the large‐scale uncertainty generated by ECMWF singular vectors with the small‐scale perturbations resolved by ALADIN breeding into atmospheric initial condition perturbations; (2) the multi‐physics approach, wherein different physics schemes are used for different forecast members to account for model uncertainties; and (3) the non‐cycling surface breeding technique, which generates surface initial condition perturbations.This article illustrates the technical details of the updated ALADIN‐LAEF and investigates its performance. Detailed verification of the upgraded ALADIN‐LAEF and a comparison with its first implementation (dynamical downscaling of ECMWF ensemble forecasts) are presented for a two‐month period in summer 2007. The results show better performance and skill for the upgraded system due to the better representation of forecast uncertainties. Copyright © 2011 Royal Meteorological Society

On the importance of initializing the stochastic part of the atmosphere for forecasting the 1997/1998 El Niño

The importance of initializing atmospheric intra-seasonal (stochastic) variations for prediction of the onset of the 1997/1998 El Nino is examined using the Australian Bureau of Meteorology coupled seasonal forecast model. A suite of 9-month forecasts was initialized on the 1st December 1996. Observed ocean initial conditions were used together with five different atmospheric initial conditions that sample a range of possible initial states of intra-seasonal (stochastic) variability, especially the Madden-Julian Oscillation (MJO), which is considered the primary stochastic variability of relevance to El Nino evolution. The atmospheric initial states were generated from a suite of atmosphere-only integrations forced by observed sea surface temperatures (SST). To the extent that low frequency variability of the tropical atmosphere is forced by slow variations in SST, these atmospheric states should all represent realistic low frequency atmospheric variability that was present in December 1996. However, to the extent that intra-seasonal variability is not constrained by SST, they should capture a range of intra-seasonal states, especially variations in the activity, phase and amplitude of the MJO. For each of these five states, a 20-member ensemble of coupled model forecasts was generated by the addition of small random perturbations to the SST field at the initial time. The ensemble mean from all five sets of forecasts resulted in El Nino but three of the sets produced substantially greater warming by months 4–5 in the NINO3.4 region compared to the other two. The warmer group stemmed from stronger intra-seasonal westerly wind anomalies associated with the MJO that propagated eastward into the central Pacific during the first 1–2 months of the forecast. These were largely absent in the colder group; the weakest of the colder group developed strong easterly wind anomalies, relative to the grand ensemble mean, that propagated into the central Pacific early in the forecast, thereby generating significantly weaker downwelling Kelvin waves in comparison to the warmer group. The strong reduction in downwelling Kelvin waves in the weakest case acted to limit the warming in the eastern Pacific, resulting in a “Modoki” type El Nino that is more focused in the central Pacific. Our results suggest that the intra-seasonal stochastic component of the atmospheric initial condition has an important and potentially predictable impact on the forecasts of the initial warming and flavour of the 1997/1998 El Nino. However, to the extent that atmospheric intra-seasonal variability is not predictable beyond a month or two, these results imply a limit to the accuracy with which the strength and perhaps the spatial distribution of an El Nino can ultimately be predicted. These results do not preclude a predictable role of the MJO and other intra-seasonal stochastic variability at longer lead times if some aspects of the stochastic variability are preconditioned by the evolving state of El Nino or other low frequency boundary forcing.

Evidence for the Molecular Basis of Corrosion of Zinc Induced by Formic Acid using Sum Frequency Generation Spectroscopy

In situ observations of surface hydroxyl groups and the subsequent ligand exchange during initial atmospheric corrosion conditions of Zn are reported. The data have been obtained with sum frequency ...

The impact of atmospheric initialisation on seasonal prediction of tropical Pacific SST

The impact of realistic atmospheric initialisation on the seasonal prediction of tropical Pacific sea surface temperatures is explored with the Predictive Ocean–Atmosphere Model for Australia (POAMA) dynamical seasonal forecast system. Previous versions of POAMA used data from an Atmospheric Model Intercomparison Project (AMIP)-style simulation to initialise the atmosphere for the hindcast simulations. The initial conditions for the hindcasts did not, therefore, capture the true intra-seasonal atmospheric state. The most recent version of POAMA has a new Atmosphere and Land Initialisation scheme (ALI), which captures the observed intra-seasonal atmospheric state. We present the ALI scheme and then compare the forecast skill of two hindcast datasets, one with AMIP-type initialisation and one with realistic initial conditions from ALI, focussing on the prediction of El Nino. For eastern Pacific (Nino3) sea surface temperature anomalies (SSTAs), both experiments beat persistence and have useful SSTA prediction skill (anomaly correlations above 0.6) at all lead times (forecasts are 9 months duration). However, the experiment with realistic atmospheric initial conditions from ALI is an improvement over the AMIP-type initialisation experiment out to about 6 months lead time. The improvements in skill are related to improved initial atmospheric anomalies rather than an improved initial mean state (the forecast drift is worse in the ALI hindcast dataset). Since we are dealing with a coupled system, initial atmospheric errors (or differences between experiments) are amplified though coupled processes which can then lead to long lasting errors (or differences).

Prediction of Monthly-Mean Temperature: The Roles of Atmospheric and Land Initial Conditions and Sea Surface Temperature

Abstract Using the retrospective forecasts from the National Centers for Environmental Prediction (NCEP) coupled atmosphere–ocean Climate Forecast System (CFS) and the Atmospheric Model Intercomparison Project (AMIP) simulations from its uncoupled atmospheric component, the NCEP Global Forecast System (GFS), the relative roles of atmospheric and land initial conditions and the lower boundary condition of sea surface temperatures (SSTs) for the prediction of monthly-mean temperature are investigated. The analysis focuses on the lead-time dependence of monthly-mean prediction skill and its asymptotic value for longer lead times, which could be attributed the atmospheric response to the slowly varying SST. The results show that the observed atmospheric and land initial conditions improve the skill of monthly-mean prediction in the extratropics but have little influence in the tropics. However, the influence of initial atmospheric and land conditions in the extratropics decays rapidly. For 30-day-lead predictions, the global-mean forecast skill of monthly means is found to reach an asymptotic value that is primarily determined by the SST anomalies. The lead time at which initial conditions lose their influence varies spatially. In addition, the initial atmospheric and land conditions are found to have longer impacts in northern winter and spring than in summer and fall. The relevance of the results for constructing lagged ensemble forecasts is discussed.