F. Vitart

MJO Simulation Diagnostics

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 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.

ERA-20C: An Atmospheric Reanalysis of the Twentieth Century

AbstractThe ECMWF twentieth century reanalysis (ERA-20C; 1900–2010) assimilates surface pressure and marine wind observations. The reanalysis is single-member, and the background errors are spatiotemporally varying, derived from an ensemble. The atmospheric general circulation model uses the same configuration as the control member of the ERA-20CM ensemble, forced by observationally based analyses of sea surface temperature, sea ice cover, atmospheric composition changes, and solar forcing. The resulting climate trend estimations resemble ERA-20CM for temperature and the water cycle. The ERA-20C water cycle features stable precipitation minus evaporation global averages and no spurious jumps or trends. The assimilation of observations adds realism on synoptic time scales as compared to ERA-20CM in regions that are sufficiently well observed. Comparing to nighttime ship observations, ERA-20C air temperatures are 1 K colder. Generally, the synoptic quality of the product and the agreement in terms of climat...

The Second Phase of the Global Land–Atmosphere Coupling Experiment: Soil Moisture Contributions to Subseasonal Forecast Skill

AbstractThe second phase of the Global Land–Atmosphere Coupling Experiment (GLACE-2) is a multi-institutional numerical modeling experiment focused on quantifying, for boreal summer, the subseasonal (out to two months) forecast skill for precipitation and air temperature that can be derived from the realistic initialization of land surface states, notably soil moisture. An overview of the experiment and model behavior at the global scale is described here, along with a determination and characterization of multimodel “consensus” skill. The models show modest but significant skill in predicting air temperatures, especially where the rain gauge network is dense. Given that precipitation is the chief driver of soil moisture, and thereby assuming that rain gauge density is a reasonable proxy for the adequacy of the observational network contributing to soil moisture initialization, this result indeed highlights the potential contribution of enhanced observations to prediction. Land-derived precipitation forec...

A Framework for Assessing Operational Madden–Julian Oscillation Forecasts: A CLIVAR MJO Working Group Project

Abstract The U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group (MJOWG) has taken steps to promote the adoption of a uniform diagnostic and set of skill metrics for analyzing and assessing dynamical forecasts of the MJO. Here we describe the framework and initial implementation of the approach using real-time forecast data from multiple operational numerical weather prediction (NWP) centers. The objectives of this activity are to provide a means to i) quantitatively compare skill of MJO forecasts across operational centers, ii) measure gains in forecast skill over time by a given center and the community as a whole, and iii) facilitate the development of a multimodel forecast of the MJO. The MJO diagnostic is based on extensive deliberations among the MJOWG in conjunction with input from a number of operational centers and makes use of the MJO index of Wheeler and Hendon. This forecast activity has been endorsed by the Working Group on Numerical Experimentation (WGNE), the internationa...

A Framework for Assessing Operational Madden–Julian Oscillation Forecasts

Abstract The U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group (MJOWG) has taken steps to promote the adoption of a uniform diagnostic and set of skill metrics for analyzing and assessing dynamical forecasts of the MJO. Here we describe the framework and initial implementation of the approach using real-time forecast data from multiple operational numerical weather prediction (NWP) centers. The objectives of this activity are to provide a means to i) quantitatively compare skill of MJO forecasts across operational centers, ii) measure gains in forecast skill over time by a given center and the community as a whole, and iii) facilitate the development of a multimodel forecast of the MJO. The MJO diagnostic is based on extensive deliberations among the MJOWG in conjunction with input from a number of operational centers and makes use of the MJO index of Wheeler and Hendon. This forecast activity has been endorsed by the Working Group on Numerical Experimentation (WGNE), the internationa...

Monthly Forecasting at ECMWF

Abstract A monthly forecasting system based on 32-day coupled ocean–atmosphere integrations has been set up at ECMWF. This system has run routinely since March 2002 every 2 weeks, and 45 cases from March 2002 to December 2003 have been verified. Results of this validation suggest that the model displays some skill in predicting weekly averaged 2-m temperature, precipitation, and mean sea level pressure anomalies relative to the climate of the past 12 years. For days 12–18, probabilistic scores indicate that the monthly forecasting system performs generally better than both climatology and the persistence of the previous weekly probabilities, suggesting that forecasts at that time range could be useful. After about 20 days of forecast, the model displays some skill in predicting events with a large threshold. At that time range, the performance of the system depends strongly on the geographical location, with Europe being a particularly difficult region. However, the model displays some useful skill after ...

Seasonal Forecasting of Tropical Storms Using Coupled GCM Integrations

The ECMWF Seasonal Forecasting System, based on ensembles of 200-day coupled GCM integrations, contains tropical disturbances that are referred to as model tropical storms in the present paper. Model tropical storms display a genesis location and a seasonal cycle generally consistent with observations, though the frequency of model tropical storms is significantly lower than observed, particularly over the North Atlantic and the eastern North Pacific. Several possible causes for the low number of model tropical storms are discussed. The ECMWF Seasonal Forecasting System produces realistic forecasts of the interannual variability of tropical storm frequency over the North Atlantic and the western North Pacific, with strong linear correlations and low rms error obtained when comparing the forecasts to observations. The skill of the seasonal forecasting system in predicting the frequency of tropical storms is likely to be related to its skill in predicting sea surface tem

The Sub-seasonal to Seasonal Prediction (S2S) Project Database

AbstractDemands are growing rapidly in the operational prediction and applications communities for forecasts that fill the gap between medium-range weather and long-range or seasonal forecasts. Based on the potential for improved forecast skill at the subseasonal to seasonal time range, the Subseasonal to Seasonal (S2S) Prediction research project has been established by the World Weather Research Programme/World Climate Research Programme. A main deliverable of this project is the establishment of an extensive database containing subseasonal (up to 60 days) forecasts, 3 weeks behind real time, and reforecasts from 11 operational centers, modeled in part on the The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database for medium-range forecasts (up to 15 days).The S2S database, available to the research community since May 2015, represents an important tool to advance our understanding of the subseasonal to seasonal time range that has been co...

Sensitivity of Atlantic Tropical Storm Frequency to ENSO and Interdecadal Variability of SSTs in an Ensemble of AGCM Integrations

A significant reduction (increase) of tropical storm activity over the Atlantic basin is observed during El Nino (La Nina) events. Furthermore, the number of Atlantic tropical storms displays an interdecadal variability with more storms in the 1950s and 1960s than in the 1970s and 1980s. Ensembles of simulations with an atmospheric general circulation model (AGCM) are used to explore the mechanisms responsible for this observed variability. The interannual variability is investigated using a 10-member ensemble of AGCM simulations forced by climatological SSTs of the 1980s everywhere except over the tropical Pacific and Indian Oceans. Significantly fewer tropical storms are simulated with El Nino SSTs imposed over the tropical Pacific and Indian Oceans than with La Nina conditions. Increased simulated vertical wind shear over the Atlantic is the most likely explanation for the reduction of simulated tropical storms during El Nino years. SST forcing from different El Nino events has distinct impacts on Atlantic tropical storms in the simulation: simulated tropical storms are significantly less numerous with 1982 SSTs imposed over the tropical Pacific and Indian Oceans than with 1986 SSTs. The interdecadal variability of tropical storm activity seems to coincide with an interdecadal variability of the North Atlantic SSTs with colder SSTs in the 1970s than in the 1950s. Ensembles of AGCM simulations produce significantly more tropical storms when forced by observed SSTs of the 1950s than when forced by SSTs of the 1970s. This supports the theory that the interdecadal variability of SSTs has a significant impact on the expected number of Atlantic tropical storms and suggests that Atlantic tropical storms may be more numerous in coming years if North Atlantic SSTs are getting warmer. A significant increase of vertical wind shear and a significant decrease in the convective available potential energy over the tropical Atlantic in the 1970s may explain the simulated interdecadal variability of Atlantic tropical storms.

Addressing model error through atmospheric stochastic physical parametrizations: impact on the coupled ECMWF seasonal forecasting system

The finite resolution of general circulation models of the coupled atmosphere–ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere–ocean climate system in operational forecast mode, and the latest seasonal forecasting system—System 4—has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981–2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden–Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid-latitude circulation regimes over the Pacific–North America region.