We present the System for High‐resolution prediction on Earth‐to‐Local Domains (SHiELD), an atmosphere model developed by the Geophysical Fluid Dynamics Laboratory (GFDL) coupling the nonhydrostatic FV3 Dynamical Core to a physics suite originally taken from the Global Forecast System. SHiELD is designed to demonstrate new capabilities within its components, explore new model applications, and to answer scientific questions through these new functionalities. A variety of configurations are presented, including short‐to‐medium‐range and subseasonal‐to‐seasonal prediction, global‐to‐regional convective‐scale hurricane and contiguous U.S. precipitation forecasts, and global cloud‐resolving modeling. Advances within SHiELD can be seamlessly transitioned into other Unified Forecast System or FV3‐based models, including operational implementations of the Unified Forecast System. Continued development of SHiELD has shown improvement upon existing models. The flagship 13‐km SHiELD demonstrates steadily improved large‐scale prediction skill and precipitation prediction skill. SHiELD and the coarser‐resolution S‐SHiELD demonstrate a superior diurnal cycle compared to existing climate models; the latter also demonstrates 28 days of useful prediction skill for the Madden‐Julian Oscillation. The global‐to‐regional nested configurations T‐SHiELD (tropical Atlantic) and C‐SHiELD (contiguous United States) show significant improvement in hurricane structure from a new tracer advection scheme and promise for medium‐range prediction of convective storms.
We present a new global‐to‐regional model, cfvGFS, able to explicitly (without parameterization) represent convection over part of the earth. This model couples the Geophysical Fluid Dynamics Laboratory Finite‐Volume Cubed‐Sphere Dynamical Core (FV3) to the Global Forecast System (GFS) physics and initial conditions, augmented with a six‐category microphysics and a modified planetary boundary layer scheme. We examine the characteristics of cfvGFS on a 3‐km continental United States domain nested within a 13‐km global model. The nested cfvGFS still has good hemispheric skill comparable to or better than the operational GFS, while supercell thunderstorms, squall lines, and derechos are explicitly‐represented over the refined region. In particular, cfvGFS has excellent representations of fine‐scale updraft helicity fields, an important proxy for severe weather forecasting. Precipitation biases are found to be smaller than in uniform‐resolution global models and competitive with operational regional models; the 3‐km domain also improves upon the global models in 2‐m temperature and humidity skill. We discuss further development of cfvGFS and the prospects for a unified global‐to‐regional prediction system.
The Caribbean low-level jet (CLLJ) is an important component of the atmospheric circulation over the Intra-Americas Sea (IAS) which impacts the weather and climate both locally and remotely. It influences the rainfall variability in the Caribbean, Central America, northern South America, the tropical Pacific and the continental Unites States through the transport of moisture. We make use of high-resolution coupled and uncoupled models from the Geophysical Fluid Dynamics Laboratory (GFDL) to investigate the simulation of the CLLJ and its teleconnections and further compare with low-resolution models. The high-resolution coupled model FLOR shows improvements in the simulation of the CLLJ and its teleconnections with rainfall and SST over the IAS compared to the low-resolution coupled model CM2.1. The CLLJ is better represented in uncoupled models (AM2.1 and AM2.5) forced with observed sea-surface temperatures (SSTs), emphasizing the role of SSTs in the simulation of the CLLJ. Further, we determine the forecast skill for observed rainfall using both high- and low-resolution predictions of rainfall and SSTs for the July–August–September season. We determine the role of statistical correction of model biases, coupling and horizontal resolution on the forecast skill. Statistical correction dramatically improves area-averaged forecast skill. But the analysis of spatial distribution in skill indicates that the improvement in skill after statistical correction is region dependent. Forecast skill is sensitive to coupling in parts of the Caribbean, Central and northern South America, and it is mostly insensitive over North America. Comparison of forecast skill between high and low-resolution coupled models does not show any dramatic difference. However, uncoupled models show improvement in the area-averaged skill in the high-resolution atmospheric model compared to lower resolution model. Understanding and improving the forecast skill over the IAS has important implications for highly vulnerable nations in the region.
This study investigates the roles of radiative forcing, sea surface temperatures (SSTs), and atmospheric and land initial conditions in the summer warming episodes of the United States. The summer warming episodes are defined as the significantly above normal (1983-2012) June-August 2-m temperature anomalies, and are referred to as heat waves in this study. Two contrasting cases, the summers of 2006 and 2012, are explored in detail to illustrate the distinct roles of SSTs, direct radiative forcing, and atmospheric and land initial conditions in driving U.S. summer heat waves. For 2012, simulations with the GFDL atmospheric general circulation model reveal that SSTs play a critical role. Further sensitivity experiments reveal the contributions of uniform global SST warming, SSTs in individual ocean basins and direct radiative forcing to the geographic distribution and magnitudes of warm temperature anomalies. In contrast, for 2006, the atmospheric and land initial conditions are key drivers. The atmospheric (land) initial conditions play a major (minor) role in the central and northwestern (eastern) U.S.. Due to changes in radiative forcing, the probability of areal-averaged summer temperature anomalies over U.S. exceeding the observed 2012 anomaly increases with time over the early 21st century. La Niña (El Niño) events tend to increase (reduce) the occurrence rate of heat waves. The temperatures over the central U.S. are mostly influenced by El Niño/La Niña, with the central tropical Pacific playing a more important role than the eastern tropical Pacific. Thus, atmospheric and land initial conditions, SSTs and radiative forcing are all important drivers of, and sources of predictability for U.S. summer heat waves.
This study demonstrates skillful seasonal prediction of 2m air temperature and precipitation over land in a new high-resolution climate model developed by Geophysical Fluid Dynamics Laboratory, and explores the possible sources of the skill. We employ a statistical optimization approach to identify the most predictable components of seasonal mean temperature and precipitation over land, and demonstrate the predictive skill of these components. First, we show improved skill of the high-resolution model over the previous lower-resolution model in seasonal prediction of NINO3.4 index and other aspects of interest. Then we measure the skill of temperature and precipitation in the high-resolution model for boreal winter and summer, and diagnose the sources of the skill. Lastly, we reconstruct predictions using a few most predictable components to yield more skillful predictions than the raw model predictions. Over three decades of hindcasts, we find that the two most predictable components of temperature are characterized by a component that is likely due to changes in external radiative forcing in boreal winter and summer, and an ENSO-related pattern in boreal winter. The most predictable components of precipitation in both seasons are very likely ENSO-related. These components of temperature and precipitation can be predicted with significant correlation skill at least 9 months in advance. The reconstructed predictions using only the first few predictable components from the model show considerably better skill relative to observations than raw model predictions. This study shows that the use of refined statistical analysis and a high-resolution dynamical model leads to significant skill in seasonal predictions of 2m air temperature and precipitation over land.
The seasonal predictability of extratropical storm tracks in Geophysical Fluid Dynamics Laboratory (GFDL)’s high-resolution climate model has been investigated using an average predictability time analysis. The leading predictable components of extratropical storm tracks are ENSO-related spatial pattern for both boreal winter and summer, and the second predictable components are mostly due to changes in external radiative forcing and multidecadal oceanic variability. These two predictable components for both seasons show significant correlation skill for all leads from 0 to 9 months, while the skill of predicting the boreal winter storm track is consistently higher than that of the austral winter. The predictable components of extratropical storm tracks are dynamically consistent with the predictable components of the upper troposphere jet flow for both seasons. Over the region with strong storm track signals in North America, the model is able to predict the changes in statistics of extremes connected to storm track changes (e.g., extreme low and high sea level pressure and extreme 2m air temperature) in response to different ENSO phases. These results point towards the possibility of providing skillful seasonal predictions of the statistics of extratropical extremes over land using high-resolution coupled models.
Decadal prediction experiments were conducted as part of CMIP5 using the GFDL-CM2.1 forecast system. The abrupt warming of the North Atlantic subpolar gyre (SPG) that was observed in the mid 1990s is considered as a case study to evaluate our forecast capabilities and better understand the reasons for the observed changes. Initializing the CM2.1 coupled system produces high skill in retrospectively predicting the mid-90s shift, which is not captured by the uninitialized forecasts. All the hindcasts initialized in the early 90s show a warming of the SPG, however, only the ensemble mean hindcasts initialized in 1995 and 1996 are able to reproduce the observed abrupt warming and the associated decrease and contraction of the SPG. Examination of the physical mechanisms responsible for the successful retrospective predictions indicates that initializing the ocean is key to predict the mid 90s warming. The successful initialized forecasts show an increased Atlantic Meridional Overturning Circulation and North Atlantic current transport, which drive an increased advection of warm saline subtropical waters northward, leading to a westward shift of the subpolar front and subsequently a warming and spin down of the SPG. Significant seasonal climate impacts are predicted as the SPG warms, including a reduced sea-ice concentration over the Arctic, an enhanced warming over central US during summer and fall, and a northward shift of the mean ITCZ. These climate anomalies are similar to those observed during a warm phase of the Atlantic Multidecadal Oscillation, which is encouraging for future predictions of North Atlantic climate.
In our original paper (Vecchi et al., 2013, hereafter V13) we stated “the skill in the initialized forecasts comes in large part from the persistence of the mid-1990s shift by the initialized forecasts, rather than from predicting its evolution”. Smith et al (2013, hereafter S13) challenge that assertion, contending that DePreSys was able to make a successful retrospective forecast of that shift. We stand by our original assertion, and present additional analyses using output from DePreSys retrospective forecasts to support our assessment.
Tropical cyclones (TCs) are a hazard to life and property and a prominent element of the global climate system, therefore understanding and predicting TC location, intensity and frequency is of both societal and scientific significance. Methodologies exist to predict basin-wide, seasonally-aggregated TC activity months, seasons and even years in advance. We show that a newly developed high-resolution global climate model can produce skillful forecasts of seasonal TC activity on spatial scales finer than basin-wide, from months and seasons in advance of the TC season. The climate model used here is targeted at predicting regional climate and the statistics of weather extremes on seasonal to decadal timescales, and is comprised of high-resolution (50km×50km) atmosphere and land components, and more moderate resolution (~100km) sea ice and ocean components. The simulation of TC climatology and interannual variations in this climate model is substantially improved by correcting systematic ocean biases through “flux-adjustment.” We perform a suite of 12-month duration retrospective forecasts over the 1981-2012 period, after initializing the climate model to observationally-constrained conditions at the start of each forecast period – using both the standard and flux-adjusted versions of the model. The standard and flux-adjusted forecasts exhibit equivalent skill at predicting Northern Hemisphere TC season sea surface temperature, but the flux-adjusted model exhibits substantially improved basin-wide and regional TC activity forecasts, highlighting the role of systematic biases in limiting the quality of TC forecasts. These results suggest that dynamical forecasts of seasonally-aggregated regional TC activity months in advance are feasible.
The Geophysical Fluid Dynamics Laboratory has developed an ensemble coupled data assimilation (ECDA) system based on the fully coupled climate model, CM2.1, in order to provide reanalyzed coupled initial conditions that are balanced with the climate prediction model. Here, we conduct a comprehensive assessment for the oceanic variability from the latest version of the ECDA analyzed for 51 years, 1960–2010. Meridional oceanic heat transport, net ocean surface heat flux, wind stress, sea surface height, top 300 m heat content, tropical temperature, salinity and currents are compared with various in situ observations and reanalyses by employing similar configurations with the assessment of the NCEP’s climate forecast system reanalysis (Xue et al. in Clim Dyn 37(11):2511–2539, 2011). Results show that the ECDA agrees well with observations in both climatology and variability for 51 years. For the simulation of the Tropical Atlantic Ocean and global salinity variability, the ECDA shows a good performance compared to existing reanalyses. The ECDA also shows no significant drift in the deep ocean temperature and salinity. While systematic model biases are mostly corrected with the coupled data assimilation, some biases (e.g., strong trade winds, weak westerly winds and warm SST in the southern oceans, subsurface temperature and salinity biases along the equatorial western Pacific boundary, overestimating the mixed layer depth around the subpolar Atlantic and high-latitude southern oceans in the winter seasons) are not completely eliminated. Mean biases such as strong South Equatorial Current, weak Equatorial Under Current, and weak Atlantic overturning transport are generated during the assimilation procedure, but their variabilities are well simulated. In terms of climate variability, the ECDA provides good simulations of the dominant oceanic signals associated with El Nino and Southern Oscillation, Indian Ocean Dipole, Pacific Decadal Oscillation, and Atlantic Meridional Overturning Circulation during the whole analyzed period, 1960–2010.
Retrospective predictions of multi-year North Atlantic hurricane frequency are explored, by applying a hybrid statistical-dynamical forecast system to initialized and non-initialized multi-year forecasts of tropical Atlantic and tropical mean sea surface temperatures (SSTs) from two global climate model forecast systems. By accounting for impacts of initialization and radiative forcing, retrospective predictions of five-year mean and nine-year mean tropical Atlantic hurricane frequency show significant correlation relative to a null hypothesis of zero correlation. The retrospective correlations are increased in a two-model average forecast and by using a lagged-ensemble approach, with the two-model ensemble decadal forecasts hurricane frequency over 1961-2011 yielding correlation coefficients that approach 0.9.
These encouraging retrospective multi-year hurricane predictions, however, should be interpreted with care: although initialized forecasts have higher nominal skill than uninitialized ones, the relatively short record and large autocorrelation of the time series limits our confidence in distinguishing between the skill due to external forcing and that added by initialization. The nominal increase in correlation in the initialized forecasts relative to the uninitialized experiments is due to improved representation of the multi-year tropical Atlantic SST anomalies. The skill in the initialized forecasts comes in large part from the persistence of a mid-1990s shift by the initialized forecasts, rather than from predicting its evolution. Predicting shifts like that observed in 1994-1995 remains a critical issue for the success of multi-year forecasts of Atlantic hurricane frequency. The retrospective forecasts highlight the possibility that changes in observing system impact forecast performance.
The decadal predictability of sea surface temperature (SST) and 2m air temperature (T2m) in Geophysical Fluid Dynamics Laboratory (GFDL)'s decadal hindcasts, which are part of the Fifth Coupled Model Intercomparison Project experiments, has been investigated using an average predictability time (APT) analysis. Comparison of retrospective forecasts initialized using the GFDL's Ensemble Coupled Data Assimilation system with uninitialized historical forcing simulations using the same model, allows identification of internal multidecadal pattern (IMP) for SST and T2m. The IMP of SST is characterized by an inter-hemisphere dipole, with warm anomalies centered in the North Atlantic subpolar gyre region and North Pacific subpolar gyre region, and cold anomalies centered in the Antarctic Circumpolar Current region. The IMP of T2m is characterized by a general bi-polar seesaw, with warm anomalies centered in Greenland, and cold anomalies centered in Antarctica. The retrospective prediction skill of the initialized system, verified against independent observations, indicates that the IMP of SST may be predictable up to 4 (10) year lead time at 95% (90%) significance level, and the IMP of T2m may be predictable up to 2 (10) years at 95% (90%) significance level. The initialization of multidecadal variations of northward oceanic heat transport in the North Atlantic significantly improves the predictive skill of the IMP. The dominant roles of oceanic internal dynamics in decadal prediction are further elucidated by fixed-forcing experiments, in which radiative forcing is returned to 1961 values. These results point towards the possibility of meaningful decadal climate outlooks using dynamical coupled models, if they are appropriately initialized from a sustained climate observing system.
Jiang, Xianan, D E Waliser, D Kim, Ming Zhao, Kenneth R Sperber, and William F Stern, et al., August 2012: Simulation of the intraseasonal variability over the Eastern Pacific ITCZ in climate models. Climate Dynamics, 39(3-4), DOI:10.1007/s00382-011-1098-x. Abstract
During boreal summer, convective activity over the eastern Pacific (EPAC) inter-tropical convergence zone (ITCZ) exhibits vigorous intraseasonal variability (ISV). Previous observational studies identified two dominant ISV modes over the EPAC, i.e., a 40-day mode and a quasi-biweekly mode (QBM). The 40-day ISV mode is generally considered a local expression of the Madden-Julian Oscillation. However, in addition to the eastward propagation, northward propagation of the 40-day mode is also evident. The QBM mode bears a smaller spatial scale than the 40-day mode, and is largely characterized by northward propagation. While the ISV over the EPAC exerts significant influences on regional climate/weather systems, investigation of contemporary model capabilities in representing these ISV modes over the EPAC is limited. In this study, the model fidelity in representing these two dominant ISV modes over the EPAC is assessed by analyzing six atmospheric and three coupled general circulation models (GCMs), including one super-parameterized GCM (SPCAM) and one recently developed high-resolution GCM (GFDL HIRAM) with horizontal resolution of about 50 km. While it remains challenging for GCMs to faithfully represent these two ISV modes including their amplitude, evolution patterns, and periodicities, encouraging simulations are also noted. In general, SPCAM and HIRAM exhibit relatively superior skill in representing the two ISV modes over the EPAC. While the advantage of SPCAM is achieved through explicit representation of the cumulus process by the embedded 2-D cloud resolving models, the improved representation in HIRAM could be ascribed to the employment of a strongly entraining plume cumulus scheme, which inhibits the deep convection, and thus effectively enhances the stratiform rainfall. The sensitivity tests based on HIRAM also suggest that fine horizontal resolution could also be conducive to realistically capture the ISV over the EPAC, particularly for the QBM mode. Further analysis illustrates that the observed 40-day ISV mode over the EPAC is closely linked to the eastward propagating ISV signals from the Indian Ocean/Western Pacific, which is in agreement with the general impression that the 40-day ISV mode over the EPAC could be a local expression of the global Madden-Julian Oscillation (MJO). In contrast, the convective signals associated with the 40-day mode over the EPAC in most of the GCM simulations tend to originate between 150°E and 150°W, suggesting the 40-day ISV mode over the EPAC might be sustained without the forcing by the eastward propagating MJO. Further investigation is warranted towards improved understanding of the origin of the ISV over the EPAC.
The Geophysical Fluid Dynamics Laboratory (GFDL) has developed a coupled general circulation model (CM3) for atmosphere, oceans, land, and sea ice. The goal of CM3 is to address emerging issues in climate change, including aerosol-cloud interactions, chemistry-climate interactions, and coupling between the troposphere and stratosphere. The model is also designed to serve as the physical-system component of earth-system models and models for decadal prediction in the near-term future, for example, through improved simulations in tropical land precipitation relative to earlier-generation GFDL models. This paper describes the dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component (AM3) of this model.
Relative to GFDL AM2, AM3 includes new treatments of deep and shallow cumulus convection, cloud-droplet activation by aerosols, sub-grid variability of stratiform vertical velocities for droplet activation, and atmospheric chemistry driven by emissions with advective, convective, and turbulent transport. AM3 employs a cubed-sphere implementation of a finite-volume dynamical core and is coupled to LM3, a new land model with eco-system dynamics and hydrology.
Most basic circulation features in AM3 are simulated as realistically, or more so, than in AM2. In particular, dry biases have been reduced over South America. In coupled mode, the simulation of Arctic sea ice concentration has improved. AM3 aerosol optical depths, scattering properties, and surface clear-sky downward shortwave radiation are more realistic than in AM2. The simulation of marine stratocumulus decks and the intensity distributions of precipitation remain problematic, as in AM2.
The last two decades of the 20th century warm in CM3 by .32°C relative to 1881-1920. The Climate Research Unit (CRU) and Goddard Institute for Space Studies analyses of observations show warming of .56°C and .52°C, respectively, over this period. CM3 includes anthropogenic cooling by aerosol cloud interactions, and its warming by late 20th century is somewhat less realistic than in CM2.1, which warmed .66°C but did not include aerosol cloud interactions. The improved simulation of the direct aerosol effect (apparent in surface clear-sky downward radiation) in CM3 evidently acts in concert with its simulation of cloud-aerosol interactions to limit greenhouse gas warming in a way that is consistent with observed global temperature changes.
Lee, June-Yi, and William F Stern, et al., September 2011: How predictable is the northern hemisphere summer upper-tropospheric circulation?Climate Dynamics, 37(5-6), DOI:10.1007/s00382-010-0909-9. Abstract
The retrospective forecast skill of three coupled climate models (NCEP CFS, GFDL CM2.1, and CAWCR POAMA 1.5) and their multi-model ensemble (MME) is evaluated, focusing on the Northern Hemisphere (NH) summer upper-tropospheric circulation along with surface temperature and precipitation for the 25-year period of 1981–2005. The seasonal prediction skill for the NH 200-hPa geopotential height basically comes from the coupled models’ ability in predicting the first two empirical orthogonal function (EOF) modes of interannual variability, because the models cannot replicate the residual higher modes. The first two leading EOF modes of the summer 200-hPa circulation account for about 84% (35.4%) of the total variability over the NH tropics (extratropics) and offer a hint of realizable potential predictability. The MME is able to predict both spatial and temporal characteristics of the first EOF mode (EOF1) even at a 5-month lead (January initial condition) with a pattern correlation coefficient (PCC) skill of 0.96 and a temporal correlation coefficient (TCC) skill of 0.62. This long-lead predictability of the EOF1 comes mainly from the prolonged impacts of El Niño-Southern Oscillation (ENSO) as the EOF1 tends to occur during the summer after the mature phase of ENSO. The second EOF mode (EOF2), on the other hand, is related to the developing ENSO and also the interdecadal variability of the sea surface temperature over the North Pacific and North Atlantic Ocean. The MME also captures the EOF2 at a 5-month lead with a PCC skill of 0.87 and a TCC skill of 0.67, but these skills are mainly obtained from the zonally symmetric component of the EOF2, not the prominent wavelike structure, the so-called circumglobal teleconnection (CGT) pattern. In both observation and the 1-month lead MME prediction, the first two leading modes are accompanied by significant rainfall and surface air temperature anomalies in the continental regions of the NH extratropics. The MME’s success in predicting the EOF1 (EOF2) is likely to lead to a better prediction of JJA precipitation anomalies over East Asia and the North Pacific (central and southern Europe and western North America).
Lee, June-Yi, Bin Wang, I-S Kang, J Shukla, Arun Kumar, Jong-Seong Kug, C E Schemm, J-J Luo, T Yamagata, X Fu, Oscar Alves, William F Stern, Anthony Rosati, and C-K Park, August 2010: How are seasonal prediction skills related to models’ performance on mean state and annual cycle?Climate Dynamics, 35(2-3), DOI:10.1007/s00382-010-0857-4. Abstract
Given observed initial conditions, how well do coupled atmosphere–ocean models predict precipitation climatology with 1-month lead forecast? And how do the models’ biases in climatology in turn affect prediction of seasonal anomalies? We address these questions based on analysis of 1-month lead retrospective predictions for 21 years of 1981–2001 made by 13 state-of-the-art coupled climate models and their multi-model ensemble (MME). The evaluation of the precipitation climatology is based on a newly designed metrics that consists of the annual mean, the solstitial mode and equinoctial asymmetric mode of the annual cycle, and the rainy season characteristics. We find that the 1-month lead seasonal prediction made by the 13-model ensemble has skills that are much higher than those in individual model ensemble predictions and approached to those in the ERA-40 and NCEP-2 reanalysis in terms of both the precipitation climatology and seasonal anomalies. We also demonstrate that the skill for individual coupled models in predicting seasonal precipitation anomalies is positively correlated with its performances on prediction of the annual mean and annual cycle of precipitation. In addition, the seasonal prediction skill for the tropical SST anomalies, which are the major predictability source of monsoon precipitation in the current coupled models, is closely link to the models’ ability in simulating the SST mean state. Correction of the inherent bias in the mean state is critical for improving the long-lead seasonal prediction. Most individual coupled models reproduce realistically the long-term annual mean precipitation and the first annual cycle (solstitial mode), but they have difficulty in capturing the second annual (equinoctial asymmetric) mode faithfully, especially over the Indian Ocean (IO) and Western North Pacific (WNP) where the seasonal cycle in SST has significant biases. The coupled models replicate the monsoon rain domains very well except in the East Asian subtropical monsoon and the tropical WNP summer monsoon regions. The models also capture the gross features of the seasonal march of the rainy season including onset and withdraw of the Asian–Australian monsoon system over four major sub-domains, but striking deficiencies in the coupled model predictions are observed over the South China Sea and WNP region, where considerable biases exist in both the amplitude and phase of the annual cycle and the summer precipitation amount and its interannual variability are underestimated.
The ability of eight climate models to simulate the Madden–Julian oscillation (MJO) is examined using diagnostics developed by the U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group. Although the MJO signal has been extracted throughout the annual cycle, this study focuses on the boreal winter (November–April) behavior. Initially, maps of the mean state and variance and equatorial space–time spectra of 850-hPa zonal wind and precipitation are compared with observations. Models best represent the intraseasonal space–time spectral peak in the zonal wind compared to that of precipitation. Using the phase–space representation of the multivariate principal components (PCs), the life cycle properties of the simulated MJOs are extracted, including the ability to represent how the MJO evolves from a given subphase and the associated decay time scales. On average, the MJO decay (e-folding) time scale for all models is shorter (20–29 days) than observations (31 days). All models are able to produce a leading pair of multivariate principal components that represents eastward propagation of intraseasonal wind and precipitation anomalies, although the fraction of the variance is smaller than observed for all models. In some cases, the dominant time scale of these PCs is outside of the 30–80-day band.
Several key variables associated with the model’s MJO are investigated, including the surface latent heat flux, boundary layer (925 hPa) moisture convergence, and the vertical structure of moisture. Low-level moisture convergence ahead (east) of convection is associated with eastward propagation in most of the models. A few models are also able to simulate the gradual moistening of the lower troposphere that precedes observed MJO convection, as well as the observed geographical difference in the vertical structure of moisture associated with the MJO. The dependence of rainfall on lower tropospheric relative humidity and the fraction of rainfall that is stratiform are also discussed, including implications these diagnostics have for MJO simulation. Based on having the most realistic intraseasonal multivariate empirical orthogonal functions, principal component power spectra, equatorial eastward propagating outgoing longwave radiation (OLR), latent heat flux, low-level moisture convergence signals, and vertical structure of moisture over the Eastern Hemisphere, the superparameterized Community Atmosphere Model (SPCAM) and the ECHAM4/Ocean Isopycnal Model (OPYC) show the best skill at representing the MJO.
The Madden–Julian oscillation (MJO) interacts with and influences a wide range of weather and climate
phenomena (e.g., monsoons, ENSO, tropical storms, midlatitude weather), and represents an important, and
as yet unexploited, source of predictability at the subseasonal time scale. Despite the important role of the
MJO in our climate and weather systems, current global circulation models (GCMs) exhibit considerable
shortcomings in representing this phenomenon. These shortcomings have been documented in a number of
multimodel comparison studies over the last decade. However, diagnosis of model performance has been
challenging, and model progress has been difficult to track, because of the lack of a coherent and standardized
set of MJO diagnostics. One of the chief objectives of the U.S. Climate Variability and Predictability
(CLIVAR) MJO Working Group is the development of observation-based diagnostics for objectively
evaluating global model simulations of the MJO in a consistent framework. Motivation for this activity is
reviewed, and the intent and justification for a set of diagnostics is provided, along with specification for their
calculation, and illustrations of their application. The diagnostics range from relatively simple analyses of
variance and correlation, to more sophisticated space–time spectral and empirical orthogonal function
analyses. These diagnostic techniques are used to detect MJO signals, to construct composite life cycles, to
identify associations of MJO activity with the mean state, and to describe interannual variability of the MJO.
We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 onetier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981–2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Ni ńo 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial–temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80–90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Nińo 3.4 SST variation. The forecasts for 2 m air temperature are better in El Nińo years than in La Nińa years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the onetier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework.
The formulation and simulation characteristics of two new global coupled climate models developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) are described. The models were designed to simulate atmospheric and oceanic climate and variability from the diurnal time scale through multicentury climate change, given our computational constraints. In particular, an important goal was to use the same model for both experimental seasonal to interannual forecasting and the study of multicentury global climate change, and this goal has been achieved.
Two versions of the coupled model are described, called CM2.0 and CM2.1. The versions differ primarily in the dynamical core used in the atmospheric component, along with the cloud tuning and some details of the land and ocean components. For both coupled models, the resolution of the land and atmospheric components is 2° latitude × 2.5° longitude; the atmospheric model has 24 vertical levels. The ocean resolution is 1° in latitude and longitude, with meridional resolution equatorward of 30° becoming progressively finer, such that the meridional resolution is 1/3° at the equator. There are 50 vertical levels in the ocean, with 22 evenly spaced levels within the top 220 m. The ocean component has poles over North America and Eurasia to avoid polar filtering. Neither coupled model employs flux adjustments.
The control simulations have stable, realistic climates when integrated over multiple centuries. Both models have simulations of ENSO that are substantially improved relative to previous GFDL coupled models. The CM2.0 model has been further evaluated as an ENSO forecast model and has good skill (CM2.1 has not been evaluated as an ENSO forecast model). Generally reduced temperature and salinity biases exist in CM2.1 relative to CM2.0. These reductions are associated with 1) improved simulations of surface wind stress in CM2.1 and associated changes in oceanic gyre circulations; 2) changes in cloud tuning and the land model, both of which act to increase the net surface shortwave radiation in CM2.1, thereby reducing an overall cold bias present in CM2.0; and 3) a reduction of ocean lateral viscosity in the extratropics in CM2.1, which reduces sea ice biases in the North Atlantic.
Both models have been used to conduct a suite of climate change simulations for the 2007 Intergovernmental Panel on Climate Change (IPCC) assessment report and are able to simulate the main features of the observed warming of the twentieth century. The climate sensitivities of the CM2.0 and CM2.1 models are 2.9 and 3.4 K, respectively. These sensitivities are defined by coupling the atmospheric components of CM2.0 and CM2.1 to a slab ocean model and allowing the model to come into equilibrium with a doubling of atmospheric CO2. The output from a suite of integrations conducted with these models is freely available online (see http://nomads.gfdl.noaa.gov/).
Manuscript received 8 December 2004, in final form 18 March 2005
for climate research developed at the Geophysical Fluid Dynamics Laboratory (GFDL) are presented. The atmosphere model, known as AM2, includes a new gridpoint dynamical core, a prognostic cloud scheme, and a multispecies aerosol climatology, as well as components from previous models used at GFDL. The land model, known as LM2, includes soil sensible and latent heat storage, groundwater storage, and stomatal resistance. The performance of the coupled model AM2–LM2 is evaluated with a series of prescribed sea surface temperature (SST) simulations. Particular focus is given to the model's climatology and the characteristics of interannual variability related to E1 Niño– Southern Oscillation (ENSO).
One AM2–LM2 integration was performed according to the prescriptions of the second Atmospheric Model Intercomparison Project (AMIP II) and data were submitted to the Program for Climate Model Diagnosis and Intercomparison (PCMDI). Particular strengths of AM2–LM2, as judged by comparison to other models participating in AMIP II, include its circulation and distributions of precipitation. Prominent problems of AM2– LM2 include a cold bias to surface and tropospheric temperatures, weak tropical cyclone activity, and weak tropical intraseasonal activity associated with the Madden–Julian oscillation.
An ensemble of 10 AM2–LM2 integrations with observed SSTs for the second half of the twentieth century permits a statistically reliable assessment of the model's response to ENSO. In general, AM2–LM2 produces a realistic simulation of the anomalies in tropical precipitation and extratropical circulation that are associated with ENSO.
Jones, C, D E Waliser, K M Lau, and William F Stern, 2004: Global occurrences of extreme precipitation and the Madden-Julian Oscillation: Observations and predictability. Journal of Climate, 17(23), 4575-4589. Abstract PDF
This study investigates 1) the eastward propagation of the Madden-Julian oscillation (MJO) and global occurrences of extreme precipitation, 2) the degree to which a general circulation model with a relatively realistic representation of the MJO simulates its influence on extremes, and 3) a possible modulation of the MJO on potential predictability of extreme precipitation events. The observational analysis shows increased frequency of extremes during active MJO phases in many locations. On a global scale, extreme events during active MJO periods are about 40% higher than in quiescent phases of the oscillation in locations of statistically significant signals.
A 10-yr National Aeronautics and Space Administration (NASA) Goddard Laboratory for the Atmospheres (GLA) GCM simulation with fixed climatological SSTs is used to generate a control run and predictability experiments. Overall, the GLA model has a realistic representation of extremes in tropical convective regions associated with the MJO, although some shortcomings also seem to be present. The GLA model shows a robust signal in the frequency of extremes in the North Pacific and on the west coast of North America, which somewhat agrees with observational studies. The analysis of predictability experiments indicates higher success in the prediction of extremes during an active MJO than in quiescent situations. Overall, the predictability experiments indicate the mean number of correct forecasts of extremes during active MJO periods to be nearly twice the correct number of extremes during quiescent phases of the oscillation in locations of statistically significant signals.
Jones, C, D E Waliser, K M Lau, and William F Stern, 2004: The Madden-Julian Oscillation and its impact on Northern Hemisphere weather predictability. Monthly Weather Review, 132(6), 1462-1471. Abstract PDF
The Madden–Julian oscillation (MJO) is known as the dominant mode of tropical intraseasonal variability and has an important role in the coupled-atmosphere system. This study uses numerical model experiments to investigate the influence of the MJO activity on weather predictability in the midlatitudes of the Northern Hemisphere. The National Aeronautics and Space Administration (NASA) Goddard Laboratory for the Atmospheres (GLA) general circulation model was used in a 10-yr simulation with fixed climatological SSTs to generate a control dataset as well as to select initial conditions for active MJO periods and “Null” cases. Two perturbation numerical experiments were performed for the 75 cases selected [(4 MJO phases + Null phase) × 15 initial conditions in each]. For each alternative initial condition, the model was integrated for 90 days. Mean anomaly correlations and standardized root-mean-square errors in the midlatitudes of the Northern Hemisphere (20°–60°N) were computed to assess predictability characteristics. The analyses of 500-hPa geopotential height, 200-hPa streamfunction, and 850-hPa zonal wind component systematically show larger predictability (~2–3 days) during periods of active MJO as opposed to quiescent episodes of the oscillation. While further studies are necessary to investigate possible model sensitivity, the results shown here highlight the importance of the MJO in modulating weather variability and show the importance of improving the representation of the MJO in operational numerical weather forecast models.
Zheng, Y, D E Waliser, William F Stern, and C Jones, 2004: The role of coupled sea surface temperatures in the simulation of the tropical intraseasonal oscillation. Journal of Climate, 17(21), 4109-4134. Abstract PDF
This study compares the tropical intraseasonal oscillation (TISO) variability in the Geophysical Fluid Dynamics Laboratory (GFDL) coupled general circulation model (CGCM) and the stand-alone atmospheric general circulation model (AGCM). For the AGCM simulation, the sea surface temperatures (SSTs) were specified using those from the CGCM simulation. This was done so that any differences in the TISO that emerged from the two simulations could be attributed to the coupling process and not to a difference in the mean background state. The comparison focused on analysis of the rainfall, 200-mb velocity potential, and 850-mb zonal wind data from the two simulations, for both summer and winter periods, and included comparisons to analogous diagnostics using NCEP–NCAR reanalysis and Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) rainfall data.
The results of the analysis showed three principal differences in the TISO variability between the coupled and uncoupled simulations. The first was that the CGCM showed an improvement in the spatial variability associated with the TISO mode, particularly for boreal summer. Specifically, the AGCM exhibited almost no TISO variability in the Indian Ocean during boreal summer—a common shortcoming among AGCMs. The CGCM, on the other hand, did show a considerable enhancement in TISO variability in this region for this season. The second was that the wavenumber -frequency spectra of the AGCM exhibited an unrealistic peak in variability at low wavenumbers (1-3, depending on the variable) and abouit 3 cycles yr-1 (cpy). This unrealistic peak of variability was absent in the CGCM, which otherwise tended to show good agreement with the observations. The third difference was that the AGCM showed a less realistic phase lag between the TISO-related convection and SST anomalies. In particular, the CGCM exhibited a near-quadrature relation between precipitation and SST anomalies, which is consistent with observations, while the phase lag was reduced in the AGCM by about 1.5 pentads (~ 1 week). The implications of the above results, including those for the notions of "perfect SST" and "two tier" experiments, are discussed, as are the caveats associated with the study's modeling framework and analysis.
Waliser, D E., K Jin, I-S Kang, William F Stern, S D Schubert, M L C Wu, K M Lau, M-I Lee, V Krishnamurthy, A Kitoh, Gerald A Meehl, V Y Galin, V Satyan, S K Mandke, Guoxiong Wu, Y Liu, and C-K Park, 2003: AGCM simulations of intraseasonal variability associated with the Asian summer monsoon. Climate Dynamics, 21(5-6), 423-446. Abstract
The intraseasonal variability associated with the Asian summer monsoon as simulated by a number of atmospheric general circulation models (AGCMs) are analyzed and assessed against observations. The model data comes from the Monsoon GCM Intercomparison project initiated by the CLIVAR/Asian-Australian Monsoon Panel. Ten GCM groups, i.e., the Center for Ocean-Land-Atmosphere Studies (COLA), Institute of Numerical Mathematics (DNM), Goddard Space Flight Center (GSFC), Geophysical Fluid Dynamics Laboratory (GFDL), Institute of Atmospheric Physics (IAP), Indian Institute of Tropical Meteorology (IITM), Meteorological Research Institute (MRI), National Center for Atmospheric Research (NCAR), Seoul National University (SNU), and the State University of New York (SUNY), participated in the intraseasonal component of the project. Each performed a set of 10 ensemble simulations for 1 September 1996-31 August 1998 using the same observed weekly SST values but with different initial conditions. The focus is on the spatial and seasonal variations associated with intraseasonal variability (ISV) of rainfall, the structure of each model''s principal mode of spatial-temporal variation of rainfall [i.e. their depiction of the Intraseasonal Oscillation (ISO)], the teleconnection patterns associated with each model''s ISO, and the implications of the models'' ISV on seasonal monsoon predictability. The results show that several of the models exhibit ISV levels at or above that found in observations with spatial patterns of ISV that resemble the observed pattern. This includes a number of rather detailed features, including the relative distribution of variability between ocean and land regions. In terms of the area-averaged variance, it is found that the fidelity of a model to represent NH summer versus winter ISV appears to be strongly linked. In addition, most models'' ISO patterns do exhibit some form of northeastward propagation. However, the model ISO patterns are typically less coherent, lack sufficient eastward propagation, and have smaller zonal and meridional spatial scales than the observed patterns, and are often limited to one side or the other of the maritime continent. The most pervasive and problematic feature of the models'' depiction of ISV and/or their ISO patterns is the overall lack of variability in the equatorial Indian Ocean. In some cases, this characteristic appears to result from some models forming double convergence zones about the equator rather than one region of strong convergence on the equator. This shortcoming results in a poor representation of the local rainfall pattern and also significantly influences the models'' representations of the global-scale teleconnection patterns associated with the ISO. Finally, analysis of the model ensemble shows a positive relationship between the strength of a model''s ISV of rainfall and its intra-ensemble variability of seasonal monsoon rainfall. The implications of this latter relation are discussed in the context of seasonal monsoon predictability.
Waliser, D E., K M Lau, William F Stern, and C Jones, 2003: Potential predictability and the Madden-Julian Oscillation. Bulletin of the American Meteorological Society, 84(1), 33-50. Abstract PDF
Ensembles of twin-predictability experiments suggest that successful dynamical forecasts of the Madden-Julian oscillation may offer one avenue for bridging the gap between medium to long-range weather forecasting and short-term climate prediction.
Waliser, D E., William F Stern, S D Schubert, and K M Lau, 2003: Dynamic predictability of intraseasonal variability associated with the Asian summer monsoon. Quarterly Journal of the Royal Meteorological Society, 129(594), 2897-2925. Abstract PDF
The objective of this study is to estimate the limit of dynamical predictability for the tropical intraseasonal oscillation (ISO) associated with the Asian summer monsoon. Ensembles of 'twin' predictability experiments were carried out with the National Aeronautics and Space Administration Goddard Laboratory for the Atmospheres atmospheric general circulation model using specified annual cycle sea surface temperatures. Initial conditions were taken from a 10-year control simulation during periods of strong ISO activity identified via extended empirical orthogonal function analysis of 30-90 day band-passed tropical rainfall. From this analysis, 21 cases were chosen for each of four distinct phases of the ISO cycle, making 84 cases in total. Two different sets of small random perturbations were added to these 84 initial states. Simulations were then performed for 90 days from each of these 168 perturbed initial conditions. A measure of potential predictability was constructed based on a ratio of the signal associated with the ISO, in terms of rainfall or 200 hPa velocity potential (VP200), and the mean-square difference between sets of twin forecasts. This ratio indicates that the limit of predictability for this model's ISO extends out to about 25 days for VP200 and to about 15 days for rainfall. The predictability measure shows modest dependence on the strength of the ISO events, with stronger events having a greater limit of predictability by a few days. It also exhibits a dependence on the phase of the ISO, with greater predictability for the convective phase at short (~> 5 days) lead times and for the suppressed phase at longer (~< 15 days) lead times. The implications of these results as well as their associated model and analysis caveats are discussed.
Kang, I-S, K Jin, K M Lau, J Shukla, V Krishnamurthy, S D Schubert, D E Waliser, William F Stern, and V Satyan, et al., 2002: Intercomparison of atmospheric GCM simulated anomalies associated with the 1997/98 El Niño. Journal of Climate, 15(19), 2791-2805. Abstract PDF
The atmospheric anomalies for the 1997/98 El Niño–Southern Oscillation (ENSO) period have been analyzed and intercompared using the data simulated by the atmospheric general circulation models (GCMs) of 11 groups participating in the Monsoon GCM Intercomparison Project initiated by the Climate Variability and Prediction Program (CLIVAR)/Asian–Australian Monsoon Panel. Each participating GCM group performed a set of 10 ensemble simulations for 1 September 1996–31 August 1998 using the same sea surface temperature (SST) conditions but with different initial conditions. The present study presents an overview of the intercomparison project and the results of an intercomparison of the global atmospheric anomalies during the 1997/98 El Niño period. Particularly, the focus is on the tropical precipitation anomalies over the monsoon–ENSO region and the upper-tropospheric circulation anomalies in the Pacific–North American (PNA) region.
Kang, I-S, K Jin, Bin Wang, K M Lau, J Shukla, V Krishnamurthy, S D Schubert, D E Waliser, and William F Stern, et al., 2002: Intercomparison of the climatological variations of Asian summer monsoon precipitation simulated by 10 GCMs. Climate Dynamics, 19(5-6), 383-395. Abstract PDF
We assess the overall performance of state-of-the-art atmospheric GCMs in simulating the climatological variations of summer monsoon rainfall over the Asian-Western Pacific region and the systematic errors that are common to a group of GCMs. The GCM data utilized are obtained from 10 GCM groups participating in the CLIVAR Monsoon GCM Intercomparison Project. The model composite shows that the overall spatial pattern of summer monsoon rainfall is similar to the observed, although the western Pacific rainfall is relatively weak. For the simulated precipitation over the western Pacific, the models can be classified into two categories. The first category of models simulates the precipitation more confined to the equatorial region and weaker precipitation in the subtropical western Pacific compared to the observed. The second category of models simulates large precipitation in the subtropical western Pacific but the region is shifted to the north by 5-10°. None of the models realistically reproduce the observed Mei-yu rain band in the region from the East China Sea to the mid Pacific. Most of the models produce a rain band along the continental side of East Asia. The climatological variations of simulated summer rainfall are examined in terms of their amplitude and their principal EOF modes. All models simulate larger amplitudes of the climatological seasonal variation of Indian summer monsoon than the observed, though most models simulate smaller amplitudes in the western Pacific. The ten model composite produces four leading EOF modes over the Asian-western Pacific region, which are remarkably similar to the observed counterparts. The first and second eigenmodes, respectively, represent the smoothed seasonal march of broad-scale monsoon and the onsets of the Indian and East Asian summer monsoon. The third and fourth modes relate to the climatological intraseasonal oscillation (CISO). In contrast to the model composite, several models fail to reproduce the first principal mode, and most models do not reproduce the observed modes higher than the second. The CISO of precipitation is also examined over the Indian monsoon and the East Asia-western Pacific monsoon regions separately.
Anderson, Jeffrey L., H van den Dool, A Barnston, W Chen, William F Stern, and Jeff J Ploshay, 1999: Present-day capabilities of numerical and statistical models for atmospheric extratropical seasonal simulation and prediction. Bulletin of the American Meteorological Society, 80(7), 1349-1361. Abstract PDF
A statistical model and extended ensemble integrations of two atmospheric general circulation models (GCMs) are used to simulate the extratropical atmospheric response to forcing by observed SSTs for the years 1980 through 1988. The simulations are compared to observations using the anomaly correlation and root-mean-square error of the 700-hPa height field over a region encompassing the extratropical North Pacific Ocean and most of North America. On average, the statistical model is found to produce considerably better simulations than either numerical model, even when simple statistical corrections are used to remove systematic errors from the numerical model simulations. In the mean, the simulation skill is low, but there are some individual seasons for which all three models produce simulations with good skill.
An approximate upper bound to the simulation skill that could be expected from a GCM ensemble, if the model's response to SST forcing is assumed to be perfect, is computed. This perfect model predictability allows one to make some rough extrapolations about the skill that could be expected if one could greatly improve the mean response of the GCMs without significantly impacting the variance of the ensemble. These perfect model predictability skills are better than the statistical model simulations during the summer, but for the winter, present-day statistical forecasts already have skill that is as high as the upper bound for the GCMs. Simultaneous improvements to the GCM mean response and reduction in the GCM ensemble variance would be required for these GCMs to do significantly better than the statistical model in winter. This does not preclude the possibility that, as is presently the case, a statistical blend of GCM and statistical predictions could produce a simulation better than either alone.
Because of the primitive state of coupled ocean-atmosphere GCMs, the vast majority of seasonal predictions currently produced by GCMs are performed using a two-tiered approach in which SSTs are first predicted and then used to force an atmospheric model; this motivates the examination of the simulation problem. However, it is straightforward to use the statistical model to produce true forecasts by changing its predictors from simultaneous to precursor SSTs. An examination of the decrease in skill of the statistical model when changed from simulation to prediction mode is extrapolated to draw conclusions about the skill to be expected from good coupled GCM predictions.
Vitart, Frederic, Jeffrey L Anderson, and William F Stern, 1999: Impact of large-scale circulation on tropical storm frequency, intensity, and location, simulated by an ensemble of GCM integrations. Journal of Climate, 12(11), 3237-3254. Abstract PDF
Tropical storms simulated by a nine-member ensemble of GCM integrations forced by observed SSTs have been tracked by an objective procedure for the period 1980-88. Statistics on tropical storm frequency, intensity, and first location have been produced. Statistical tools such as the chi-square and the Kolmogorov-Smirnov test indicate that there is significant potential predictability of interannual variability of simulated tropical storm frequency, intensity, and first location over most of the ocean basins. The only common point between the nine members of the ensemble is the SST forcing. This implies that SSTs play a fundamental role in model tropical storm frequency, intensity, and first location interannual variability. Although the interannual variability of tropical storm statistics is clearly affected by SST forcing in the GCM, there is also a considerable amount of noise related to internal variability of the model. An ensemble of atmospheric model simulations allows one to filter this noise and gain a better understanding of the mechanisms leading to interannual tropical storm variability.
An EOF analysis of local SSTs over each ocean basin and a combined EOF analysis of vertical wind shear, 850-mb vorticity, and 200-mb vorticity have been performed. Over some ocean basins such as the western North Atlantic, the interannual frequency of simulated tropical storms is highly correlated to the first combined EOF, but it is not significantly correlated to the first EOF of local SSTs. This suggests that over these basins the SSTs have an impact on the simulated tropical storm statistics from a remote area through the large-scale circulation as in observations. Simulated and observed tropical storm statistics have been compared. The interannual variability of simulated tropical storm statistics is consistent with observations over the ocean basins where the model simulates a realistic interannual variability of the large-scale circulation.
Anderson, Jeffrey L., H van den Dool, A Barnston, W Chen, William F Stern, and Jeff J Ploshay, 1998: Capabilities of dynamical and statistical methods for atmospheric extratropical seasonal prediction In Proceedings of the Twenty-Second Annual Climate Diagnostics and Prediction Workshop, Springfield, VA, NTIS, 46-49.
D'Andrea, F, S Tibaldi, M Blackburn, G J Boer, M Dequé, Martin R Dix, B Dugas, L Ferranti, and William F Stern, et al., 1998: Northern Hemisphere atmospheric blocking as simulated by 15 atmospheric general circulation models in the period 1979-1988. Climate Dynamics, 14(6), 385-407. Abstract PDF
As a part of the Atmospheric Model Intercomparison Project (AMIP), the behaviour of 15 general circulation models has been analyzed in order to diagnose and compare the ability of the different models in simulating Northern Hemisphere midlatitude atmospheric blocking. In accordance with the established AMIP procedure, the 10-year model integrations were performed using prescribed, time-evolving monthly mean observed SSTs spanning the period January 1979 - December 1988. Atmospheric observational data (ECMWF analyses) over the same period have been also used to verify the model results. The models involved in this comparison represent a wide spectrum of model complexity, with different horizontal and vertical resolution, numerical techniques and physical parameterizations, and exhibit large differences in blocking behaviour. Nevertheless, a few common features can be found, such as the general tendency to underestimate both blocking frequency and the average duration of blocks. The problem of the possible relationship between model blocking and model systematic errors has also been assessed, although without resorting to ad-hoc numerical experimentation it is impossible to relate with certainty particular model deficiencies in representing blocking to precise parts of the model formulation.
Stern, William F., and Anthony Rosati, 1998: Issues of orographic adjustment of SSTs in a spectral/coupled GCM In Research Activities in Atmospheric and Oceanic Modelling, Report No. 27, WMO/TD-No. 865, Geneva, Switzerland, World Meteorological Organization, 6.21-6.22.
Vitart, Frederic, Jeffrey L Anderson, and William F Stern, 1998: Evaluation of the skill of an ensemble of GCM integrations in simulating seasonal tropical storm frequency, intensity and location In Proceedings of the Twenty-Second Annual Climate Diagnostics and Prediction Workshop, Springfield, VA, NTIS, 38-41.
Vitart, Frederic, Jeffrey L Anderson, and William F Stern, 1998: Simulation of the internally variability of tropical storm frequency, intensity and location in an ensemble of GCM integrations In Research Activities in Atmospheric and Oceanic Modelling, WMO/TD No. 865, Geneva, Switzerland, World Meteorological Organization, 6.28-6.29.
Yang, X-Q, Jeffrey L Anderson, and William F Stern, 1998: Reproducible forced modes in AGCM ensemble integrations and potential predictability of atmospheric seasonal variations in the extratropics In Proceedings of the Twenty-Second Annual Climate Diagnostics and Prediction Workshop, Springfield, VA, NTIS, 50-53.
Yang, X-Q, Jeffrey L Anderson, and William F Stern, 1998: Reproducible forced modes in AGCM ensemble integrations and potential predictability of atmospheric seasonal variations in the extratropics. Journal of Climate, 11(11), 2942-2959. Abstract PDF
An approach to assess the potential predictability of the extratropical atmospheric seasonal variations in an ensemble of atmospheric general circulation model (AGCM) integrations has been proposed in this study by isolating reproducible forced modes and examining their contributions to the local ensemble mean. The analyses are based on the monthly mean output of an eight-member ensemble of 10-yr Atmospheric Model Intercomparison Project integrations with a T42L18 AGCM.
An EOF decomposition applied to the ensemble anomalies shows that there exist some forced modes that are less affected by the internal process and thus appear to be highly reproducible. By reconstructing the ensemble in terms of the more reproducible forced modes and by developing a quantitative measure, the potential predictability index (PPI), which combines the reproducibility with the local variance contribution, the local ensemble mean over some selective geographic areas in the extratropics was shown to result primarily from reproducible forced modes rather than internal chaotic fluctuations. Over those regions the ensemble mean is potentially predictable. Extratropical potentially predictable regions are found mainly over North America and part of the Asian monsoon regions. Interestingly, the potential predictability over some preferred areas such as Indian monsoon areas and central Africa occasionally results primarily from non-ENSO-related boundary forcing, although ENSO forcing generally dominates over most of the preferred areas.
The quantitative analysis of the extratropical potential predictability with PPI has shown that the preferred geographic areas have obvious seasonality. For the 850-hPa temperature, for example, potentially predictable regions during spring and winter are confined to Alaska, northwest Canada, and the southeast United States, the traditional PNA region, while during summer and fall they are favored over the middle part of North America. It has also been shown that the boreal summer season (June-August) possesses the largest potentially predictable area, which seems to indicate that it is a favored season for the extratropical potential predictability. On the contrary, boreal winter (December-February) appears to have a minimum area of extratropical potential predictability.
The results have been compared with the more traditional statistical tests for potential predictability and with observations from the National Centers for Environmental Prediction reanalysis, which indicates that the PPI analysis proposed here is successful in revealing extratropical potential predictability determined by the external forcing.
Stern, William F., 1997: Investigations into the role of tropical intraseasonal oscillations in seasonal NWP In CAS/JSC Working Group on Numerical Experimentation - Research Activities in Atmospheric & Oceanic Modelling, WMO/ICSU/IOC World Climate Research Programme, Report No. 25, WMO/TD-No. 792, Geneva, Switzerland, World Meteorological Organization, 8.59-8.60.
Vitart, Frederic, Jeffrey L Anderson, and William F Stern, 1997: Simulation of interannual variability of tropical storm frequency in an ensemble of GCM integrations. Journal of Climate, 10(4), 745-760. Abstract PDF
The present study examines the simulation of the number of tropical storms produced in GCM integrations with a prescribed SST. A 9-member ensemble of 10-yr integrations (1979-88) of a T42 atmospheric model forced by observed SSTs has been produced; each ensemble member differs only in the initial atmospheric conditions. An objective procedure for tracking model-generated tropical storms is applied to this ensemble during the last 9 yrs of the integrations (1980-88). The seasonal and monthly variations of tropical storm numbers are compared with observations for each ocean basin.
Statistical tools such as the Chi-square test, the F test, and the t test are applied to the ensemble number of tropical storms, leading to the conclusion that the potential predictability is particularly strong over the western North Pacific and the eastern North Pacific, and to a lesser extent over the western North Atlantic. A set of tools including the joint probability distribution and the ranked probability score are used to evaluate the simulation skill of this ensemble simulation. The simulation skill over the western North Atlantic basin appears to be exceptionally high, particularly during years of strong potential predictability.
Anderson, Jeffrey L., and William F Stern, 1996: Evaluating the potential predictive utility of ensemble forecasts. Journal of Climate, 9(2), 260-269. Abstract PDF
A method is presented for determining when an ensemble of model forecasts has the potential to provide some useful information. An ensemble forecast of a particular scalar quantity is said to have potential predictive utility when the ensemble forecast distribution is significantly different from an appropriate climatological distribution. Here, the potential predictive utility is measured using Kuiper's statistical test for comparing two discrete distributions. More traditional measures of the potential usefulness of an ensemble forecast based on ensemble mean or variance discard possibly valuable information by making implicit assumptions about the distributions being compared
Application of the potential predictive utility to long integrations of an atmospheric general circulation model in a boundary value problem (an ensemble of Atmospheric Model Intercomparison Project integrations) reveals a number of features about the response of a GCM to observed sea surface temperatures. In particular, the ensemble of forecasts is found to have potential predictive utility over large geographic areas for a number of atmospheric fields during strong El Niño-Southern Oscillation anomalous events. Unfortunately, there are only limited areas of potential predictive utility for near-surface fields and precipitation outside the regions of the tropical oceans. Nevertheless, the method presented here can identify all areas where the GCM ensemble may provide useful information, whereas methods that make assumptions about the distribution of the ensemble forecast variables may not be able to do so.
D'Andrea, F, and William F Stern, et al., 1996: Northern Hemisphere Atmospheric Blocking as Simulated by 15 Atmospheric General Circulation Models in the Period 1979-1988 (Results from an AMIP Diagnostic Subproject, WCRP-96. WMO/TD-No. 784, WMO, Geneva, Switzerland: World Meteorological Organization, 25 pp. Abstract
As a part of the Atmospheric Model Intercomparison Project (AMIP), the behavior of 15 General Circulation Models has been analyzed in order to diagnose and compare the ability of the different models in simulating midlatitude atmospheric blocking. In accordance to the established AMIP procedure, the 10-year model integrations were performed using prescribed, time evolving monthly mean observed SSTs spanning the period January 1979- December 1988. Atmospheric observational data (ECMWF analysis) over the same period have been also used to verify the models results.
The models involved in this comparison represent a wide spectrum of model complexity, with different horizontal and vertical resolution, numerical techniques and physical parameterizations, and exhibit large differences in blocking behavior. Nevertheless, a few common features can be found, such as the general tendency to underestimate both blocking frequency and the average duration of blocks.
The relation between model blocking and systematic errors has also been assessed, although without resorting to ad-hoc numerical experimentation it is impossible to relate with certainty particular model deficiencies in representing blocking to precise parts of the model formulation.
Stern, William F., and Jeffrey L Anderson, 1996: Interannual variability of tropical intraseasonal oscillations in the GFDL/DERF GCM inferred from an ensemble of AMIP integrations In 11th Conference on Numerical Weather Prediction, Boston, MA, American Meteorological Society, 15-16.
Stern, William F., and C Tony Gordon, 1996: Specifying and modeling snow cover in multi-year GCM integrations In Research Activities in Atmospheric and Oceanic Modelling, CAS/JSC Working Group on Numerical Experimentation, Report No. 23 WMO/TD No. 734, World Meteorological Organization, 4.43-4.44.
Vitart, Frederic, Jeffrey L Anderson, and William F Stern, 1996: Potential predictability of tropical storms in an ensemble of forecasts In Proceedings of the 20th Annual Climate Diagnostics Workshop, U.S. Dept. of Commerce/NOAA/NWS, 263-266.
Vitart, Frederic, Jeffrey L Anderson, and William F Stern, 1996: Potential predictability of tropical storms in ensemble GCM simulations In Research Activities in Atmospheric and Oceanic Modelling, CAS/JSC Working Group on Numerical Experimentation, Report No. 23 WMO/TD No. 734, World Meteorological Organization, 6.32.
Anderson, Jeffrey L., and William F Stern, 1995: A method of evaluating the predictive ability of ensemble forecasts In Proceedings of the 19th Annual Climte Diagnostics Workshop, Springfield, VA, NTIS, 472-475.
Gleckler, Peter J., Kikuro Miyakoda, and William F Stern, et al., 1995: Cloud-radiative effects on implied oceanic energy transports as simulated by atmospheric general circulation models. Geophysical Research Letters, 22(7), 791-794. Abstract PDF
This paper summarizes the ocean surface net energy flux simulated by fifteen atmospheric general circulation models constrained by realistically-varying sea surface temperatures and sea ice as part of the Atmospheric Model Intercomparison Project. In general, the simulated energy fluxes are within the very large observational uncertainties. However, the annual mean oceanic meridional heat transport that would be required to balance the simulated surface fluxes is shown to be critically sensitive to the radiative effects of clouds, to the extent that even the sign of the Southern Hemisphere ocean heat transport can be affected by the errors in simulated cloud-radiation interactions. It is suggested that improved treatment of cloud radiative effects should help in the development of coupled atmosphere-ocean general circulation models.
Miyakoda, Kikuro, Joseph J Sirutis, Anthony Rosati, C Tony Gordon, Richard G Gudgel, William F Stern, Jeffrey L Anderson, and A Navarra, 1995: Atmospheric parameterizations in coupled air-sea models used for forecasts of ENSO In Proceedings of the International Scientific Conference on the Tropical Ocean Global Atmosphere (TOGA) Programme, WCRP-91, WMO/TD No. 717, Geneva, Switzerland, World Meteorological Organization, 802-806. Abstract
In order to investigate the feasibility of seasonal forecasts, a prediction system is developed. Here the main theme is the study of atmospheric physics parameterization for coupled air-sea modeling. The oceanic GCM uses 1 degree global grid with a finer resolution in the equatorial belt. The atmospheric GCM has the spectral T30 representation, which includes all of the usual physics parameterizations. Using a first version of the model (Coupled Model I) and a set of appropriate initial conditions, the capability of El Niño and La Niña forecasting with a 13 month lead time was tested, resulting in successful forecasts of the 1982/83 and 1988/89 events (Rosati et al., 1995b). However, longer runs of this system have revealed a sizable systematic error in simulations with a tendency to cool most of the world ocean, particularly the western tropical Pacific, and also without an adequate annual cycle of the SST in the eastern tropical Pacific.
In order to improve some of these features, particularly the ENSO phenomena, various versions of the atmospheric parameterizations and mountain representation are incorporated into the atmospheric GCM, and the model simulations are examined. The experiments are divided into two steps: one is with the uncoupled atmospheric model, and the other is with the coupled model. In the first step, five year simulations are carried out with the observed SST prescribed, and the results are compared with observations, which enables one to make the critical validation of the model. The second step is to couple the atmospheric and oceanic models, and integrate them from a January 1982 initial condition for 7 years, and also for another initial condition, i.e., January, 1988 for 13 months.
Compared with the boundary forced simulation, the coupling process introduces more degree of freedom, with increase of the sensitivity as well as the complexity considerably. In particular, the El Niño simulation is sensitive to any change of physics. For this reason, the objective of the simulation is focused only on the equatorial Pacific process and secondly the Indian monsoon, as opposed to the overall improvement of the general circulation. In other words, the approach is close to that of mechanistic modeling with specific targets rather than that of a GCM with broader objectives. The research is proceeding in two directions. One is: investigating the model's sensitivity for El Niño and La Niña processes to variation in a coupling parameter. The second is: after a number of trial-and-error experiments on various combinations of the parameterizations, the second atmospheric model, i.e., Model II, is selected. It is shown that Coupled Model II performs substantially better in some aspects but worse in other aspects than Coupled Model I. The improvement is found in the SST: warming occurs not only over the equatorial Pacific but also over the whole globe. The SST increase is achieved by the strong effect of the cumulus convection. On the other hand, some deficiencies remain the same in both models, i.e., the large positive errors of the SST in the eastern oceans, the lack of an annual cycle of the SST in the eastern equatorial Pacific, and the failure in forecast of the second El Niño. In summary, the prediction of the Southern Oscillation has been achieved by the two models for a full first cycle but not for the second cycle .
Stern, William F., 1995: Tropical intraseasonal variability in the GFDL/DERF GCM during AMIP In Proceedings of the First International AMIP Scientific Conference, WCRP-92, WMO/TD No. 273, Geneva, Switzerland, World Meteorological Organization, 137-142.
Assuming that SST provides the major lower boundary forcing for the atmosphere, observed SSTs are prescribed for an ensemble of atmospheric general circulation model (GCM) simulations. The ensemble consists of 9 "decadal" runs with different initial conditions chosen between 1 January 1979 and 1 January 1981 and integrated about 10 years. The main objective is to explore the feasibility of seasonal forecasts using GCMs. The extent to which the individual members of the ensemble reproduce the solutions of each other (i.e., reproducibility) may be taken as an indication of potential predictability. In addition, the ability of a particular GCM to produce realistic solutions, when compared with observations, must also be addressed as part of the predictability problem.
A measure of reproducibility may be assessed from the spread among ensemble members. A normalized spread index, can be defined at any point in space and time, as the variability of the ensemble normalized by the climatological seasonal variability. In the time mean it is found that the reproducibility is significantly below unity for certain regions. Low values of the spread index are seen generally in the Tropics, whereas the extratropics does not exhibit a high degree of reproducibility. However, if one examines plots in time of seasonal mean for the U.S. region, for example, it is found that for certain periods this index is much less than unity, perhaps implying "occasional potential predictability." In this regard, time series of ensemble mean soil moisture and precipitation over the United States are compared with corresponding observations. This study reveals some marginal skill in simulating periods of drought and excessive wetness over the United States during the 1980s (i.e., the droughts of 1981 and 1988, and the excessive wetness during the 1982/83 El Niño). In addition, by focusing on regions of better time-averaged reproducibility - that is, the southeast United States and northeast Brazil - a clearer indication of a relationship between good reproducibility and seasonal predictability seems to emerge.
Stern, William F., and Kikuro Miyakoda, 1995: Interannual variability and reproducibility from multiple GCM simulations In Proceedings of the 19th Annual Climate Diagnostics Workshop, Springfield, VA, NTIS, 92-95.
The reanalysis of FGGE [First GARP (Global Atmospheric Research Program) Global Experiment] data for 128 days during two special observing periods has been performed, using an improved data-assimilation system and the revised FGGE level II dataset. The data-assimilation scheme features forward continuous (in time) data injection in both the original and the new systems. However, the major revisions in the new system include a better first guess and a more efficient dynamical balancing for the assimilation of observed data. The results of the implementation of this system are assessed by intercomparisons among the new FGGE analysis of other institutions such as ECMWF (European Centre for Medium-Range Weather Forecasts) and NMC (National Meteorological Center, Washington, D.C.), and also the original GFDL (Geophysical Fluid Dynamics Laboratory) analysis. The quality of the new GFDL analysis is now comparable to those of the other two institutions. However, the moisture analysis appears to be appreciably different, suggesting that the cumulus convection parameterizations and the boundary-layer moisture fluxes in the models are responsible for this discrepancy.
A detailed investigation of the results has been carried out by comparing the analyses with radiosonde observations. This verification reveals that temperature and wind differences have been reduced considerably from the original to the new GFDL analysis; they are now competitive with those of ECMWF and NMC, while with regard to the geopotential height, differences of the GFDL reanalysis are larger than the original GFDL as well as the ECMWF and the NMC. A comparative study is also made with UCLA analyses over Asia in connection with the Indian monsoon. The results indicate that the qualities of both analyses are comparable. The capability of representing Madden-Julian oscillations in the reanalysis and in the ECMWF and old GFDL analysis is investigated by comparing with satellite observations. It is revealed that these oscillations are successfully reproduced by the new analysis; however, the agreement with the satellite data is not quite satisfactory. The utilization of satellite-observed wind (satobs) and aircraft data (aireps) in the data assimilation needs particular care. It appears that the quality control of these data in the GFDL reanalysis is too restrictive; in other words, the toss-out criterion of wind data is too small. A consequence of the failure to accept some single-level data turns out to be a fairly large discrepancy in representing the maximum wind speed in the analysis. It is also discussed that the current forward continuous-injection scheme is not adequate to obtain diabatic quantities for the archive.
Major revisions to the Geophysical Fluid Dynamics Laboratory's (GFDL) continuous data assimilation system have been implemented and tested. Shortcomings noted during the original processing of data from FGGE [First GARP (Global Atmospheric Research Program) Global Experiment ] served as the basis for these improvements. This new system has been used to reanalyze the two FGGE special observing periods. The main focus here will be on assessing the changes to the assimilation system using comparisons of rerun test results with results from the original FGGE processing.
The key new features in the current system include: a reduction in the assimilation cycle from 12 to 6 h; the use of a 6-h forecast first guess for the OI (optimum-interpolation analysis) as opposed to the previous use of persistence as a first guess; an extension of the OI search range from 250 to 500 km with an increase in the maximum number of observations used per analysis point from 8 to 12; the introduction of incremental linear normal-mode initialization, eliminating the periodic nonlinear normal-mode initialization; and an increase in the horizontal resolution of the assimilating model from 30 waves to 42 waves, rhomboidally truncated. Tests of the new system show a significant reduction in the level of noise, improved consistency between mass and momentum analyses, and a better fit of the analyses to observations. In addition, the new system has demonstrated a greater ability to resolve rapidly moving and deepening transient features, with an indication of less rejection of surface pressure data.
In addition to the quantities archived during the original FGGE data processing, components of diabatic heating from the assimilating model have also been archived. They should be used with caution to the extent that they reflect model bias and spinup in addition to real features of the general circulation.
Stern, William F., and Kikuro Miyakoda, 1988: Systematic errors in GFDL's extended range prediction spectral GCM In Workshop on Systematic Errors in Models of the Atmosphere, CAS/JSC Working Group on Numerical Experimentation, Report No. 12, WMO/TD No. 273, World Meteorological Organization, 78-85.
Stern, William F., R T Pierrehumbert, Joseph J Sirutis, Jeff J Ploshay, and Kikuro Miyakoda, 1986: Recent developments in the GFDL extended-range forecasting system In Short- and Medium-Range Numerical Weather Prediction, Collection of papers presented at the WMO/IUGG NWP Symposium, World Meteorological Organization, 359-363. Abstract
An assessment is made of the areas of focus for improving extended-range forecasting. Two topics currently being researched involve the reduction of systematic error by improving a GCM's accuracy and the refinement of the transition between the data assimilation phase and the forecasting phase.
Subgrid-scale orographic parameterizations have been the subject of recent model improvement activities. Results are shown for an envelope orography with an N48L9 gridpoint model and using a mountain gravity wave drag scheme with an R42L18 spectral model. In both cases there is an encouraging reduction in the systematic errors.
Proper initialization of tropical features, i.e., 40-50 day waves, may be crucial for extended-range predictions in the extra-tropics as well as the tropics. Using a continuous data assimilation scheme the 40-50 day oscillations in the tropics appear to be well maintained from the assimilation to the forecast phase. However, the assimilation system underestimates precipitation and evaporation rates.
Gordon, C T., William F Stern, and R D Hovanec, 1985: A simple scheme for generating two layers of radiatively constrained effective clouds in GCMs. Journal of Geophysical Research, 90(D6), 10,563-10,585. Abstract PDF
GCM-dependent, radiatively constrained cloud amount fields could be preferable to currently available observed fields for calculating radiative fluxes in GCM's used in long-range weather forecast studies. Motivated by this premise, we formulate an economical effective cloud algorithm for GCM's called "SATCLD," which utilizes compact, readily accessible analyses of observed satellite-derived radiative flux data. We then examine the plausibility of preliminary effective cloud fields. Analysis of SATCLD and other cloud fields and associated radiative fluxes (diagnosed by our GCM's cloud radiation model from observed atmospheric temperature and water vapor data) also provides some insight into biases in our GCM's cloud radiation model and surface albedo field. "SATCLD" generates effective low and high cloud amounts at each GCM grid point by minimizing the sum of the squares of the local residual (i.e., model-diagnosed minus observed) shortwave and longwave radiative fluxes. The preliminary SATCLD effective cloud amount fields seem plausible in many respects, based upon comparison with the satellite-derived 3DNEPH and surface-based SFCOBS analyses. In the tropics, the SATCLD effective high cloud amount is rather well correlated with 3DNEPH, while systematic differences in low cloud amount are evident in July. Off the west coasts of Central and South America and southern Africa, the SATCLD effective low cloud resembles SFCOBS in July. At mid-latitudes, the strongest similarities are between SATCLD versus 3DNEPH high cloud amount in July and SATCLD versus SFCOBS low cloud amount over the oceans. The SATCLD analysis is ill conditioned in the polar night region. Limitations of the present scheme as well as deficiencies in our GCM's cloud-radiation model and surface albedo fields and in the archived satellite data are discussed. Suggestions are made for reducing discrepancies between effective versus real clouds without sacrificing consistency between GCM-diagnosed versus observed radiative fluxes.
Gordon, C T., R D Hovanec, and William F Stern, 1984: Analyses of monthly mean cloudiness and their influence upon model-diagnosed radiative fluxes. Journal of Geophysical Research, 89(D3), 4713-4738. Abstract PDF
Two different monthly mean analyses of low, middle, and high cloud amounts for January 1977 and July 1979 are compared: 3DNEPH is a condensed version (northern hemisphere only) of the Air Force 3D-Neph analysis, which incorporates satellite data plus surface observations of clouds and auxiliary meteorological data. SFCOBS is objectively analyzed from surface observations of clouds. The SFCOBS and 3DNEPH analyses of low cloud amounts agree qualitatively in the winter extratropics. The 3DNEPH ITCZ is much more sharply defined than the SFCOBS. The sensitivity of radiative fluxes to 3DNEPH, SFCOBS, and zonal mean 3DNEPH clouds is then evaluated. The fluxes are diagnosed by a cloud-radiation model utilizing "observed" monthly mean temperature and water vapor fields and are verified against satellite data. The outgoing longwave radiative flux clearly verifies best for 3DNEPH clouds and worst for zonal mean 3DNEPH clouds in the tropics. It is predominately controlled by surface temperature in the winter extratropics. Generally speaking, the shortwave fluxes do not verify as well as the longwave fluxes. Also, outside of the winter extratropics, the net radiative fluxes correlate poorly with observation. Biases in the zonal mean long and shortwave fluxes can be reduced by adjusting other cloud-related parameters. Based upon the above results, it may be worthwhile to construct a monthly mean cloud climatology from a condensed version of the 3D-Neph. However, alternative strategies should also be explored, such as the development of cloud analysis schemes that constrain the model-diagnosed net radiative flux to be consistent with observation.
Gordon, C T., and William F Stern, 1984: Medium range prediction by a GFDL global spectral model: results for three winter cases and sensitivity to dissipation. Monthly Weather Review, 112(2), 217-245. Abstract PDF
A preliminary evaluation is made of the medium range predictive capability of a GFDL global spectral model of the atmosphere, based upon three winter blocking cases. Analogous forecasts by a GFDL global grid point model provide a background standard of comparison. The spectral model is rhomboidally truncated at wavenumber 30, has 9 sigma levels, incorporates sub-grid scale physical processes commonly associated with general circulation models and employs semi-implicit time differencing. The grid point model has somewhat finer horizontal resolution and fairly similar sub-grid scale physical processes, and employs explicit time differencing. The spectral model is up to 6 times more economical. The level of forecast skill for the 5 to 15 day range is generally less than practically useful and is more case-dependent than spectral versus grid point model-dependent. In the most successful case, i.e., 16 January 1979, an observed Atlantic blocking ridge is simulated quite well, especially by the spectral model. The predicted Atlantic ridges tend to retard approaching upstream transient disturbances. A zonal bias of the midlatitude circulation, which develops in all three spectral and grid point model predictions is most pronounced in the spectral model forecast from 1 January 1977. Results of a diffusion sensitivity experiment and other evidence suggest that insufficient frictional dissipation may have enhanced the zonal bias of the above forecast. The bias diminishes, consistent with a redistribution of spectral kinetic energy among zonal wavenumbers 0, 1 and 2, if a static stability-dependent parameterization of vertical mixing or stronger Δ4 horizontal diffusion are used. Also, the predicted-enstrophy spectrum at midlatitudes steepens, given the stronger Δ4 horizontal diffusion.
January 1977 was a month noted for its extraordinary weather over North America. The winter was dominated by two persistent large amplitude ridges positioned over the west coast of North America and the Icelandic region of the Atlantic Ocean. A very intense trough reached deep into the eastern United States and caused one of the coldest Januaries on record. One-month integrations of various GCMs were conducted in order to test their ability to simulate this blocking event. Reasonably high resolution finite difference and spectral models available at GFDL were used. Each GCM was integrated from three different analyses of the initial conditions. For some models, a fairly accurate forecast was obtained and considerable skill was recognized in the simulation of the 30-day evolution in terms of the 5-day or 10-day mean flow fields, including the period of record breaking coldness over the eastern United States. The main conclusion is that proper treatment of the subgrid-scale processes as well as sufficient spatial resolution are essential for the simulations of this phenomenon as an initial value problem. Weak zonal wind poleward of about 40 degrees N and upstream of the blocking ridge appears to be crucial for the successful simulation of the sustained blocking ridge.
A multi-level, global, spectral transform model of the atmosphere, based upon spherical harmonics, has been developed at GFDL. The basic model has nine sigma levels in the vertical and rhomboidal spectral truncation at wavenumber 30. However, finer spectral or vertical resolution versions are available as well. The model's efficient semi-implicit time differencing scheme does not appear to adversely affect medium range predictions. The model has physical processes commonly associated with grid point GCM's. Two unique features are a linearized virtual temperature correction and an optional, spectrally-computed non-linear horizontal diffusion scheme. A parameterization of vertical mixing based upon the turbulent closure method is also optional.
The GFDL spectral model has been widely utilized at GFDL for extended range weather prediction experiments. In addition, it has been adapted and applied to climate studies, four-dimensional data assimilation experiments and even to the atmosphere of Venus. These applications are briefly reviewed.
Gordon, C T., and William F Stern, 1982: Sensitivity of GCM-diagnosed radiative fluxes to "observed" monthly mean distributions of cloudiness In Proceedings of the Sixth Annual Climate Diagnostics Workshop, Washington, DC, NOAA, 268-278.
