1. Relationship between interannual variations in the Length of Day (LOD) and ENSO C. Endler, P. Névir, G.C. Leckebusch, U. Ulbrich and E. Lehmann Contact: christina.endler@met.fu-berlin.de, Institut für Meteorologie, Carl-Heinrich-Becker-Weg 6-10, 12165 Berlin, www.met.fu-berlin.de Institut für Meteorologie Introduction Summary and outlook El Niño and the Southern Oscillation represent two coupled large scale phenomena of the atmosphere- ocean system. El Niño (La Niña) events are characterized through warmer (cooler) sea surface temperatures (SST) along the equatorial East Pacific. Those SST anomalies modify dominant winds while causing a change of the general atmospheric circulation. Typically, during El Niño (La Niña) events a decline (raise) in the strength of the Trade winds is observed. Consequently, the atmospheric angular momentum and thus the LOD is increasing (decreasing). Cf Fig. 2 This observation supports a well correlated relation between interannual variations in the AAM and LOD with the occurrence of ENSO events. Hence associated mass transports within the atmosphere and oceans modify the solid earth’s angular momentum to conserve momentum. Data and methods ENSO (El Niño-Southern Oscillation) This study so far has demonstrated a highly variable influence of ocean and atmosphere on LOD with respect to ENSO. The correlation structure between an independent parameter (LOD) and ENSO sensitive variables (SST, AAM) emphasizes the use of LOD as a potential climate indicator. Ongoing investigations focus on underlying processes and mechanisms (e.g. momentum transport) to reveal causes of this inter-ENSO variability. EOP C04 LOD-time series AAM calculations based on wind fields by ECMWF ERA40 (European Centre for Medium-Range Weather Forecasts) ENSO Indices: SOI (Southern Oscillation Index: surface pressure difference Tahiti - Darwin) (http://www.cpc.ncep.noaa.gov/data/indices/soi) NINO3.4 (Sea Surface Temperatures and associated anomalies) (http://www.cpc.ncep.noaa.gov/data/indices/sstoi.indices) MEI (Multivariate ENSO-Index) (http://www.cdc.noaa.gov/people/klaus.wolter/MEI/table.html) The mean annual cycle has been removed from all time series. Also, long-term variations in LOD time series were filtered (cut off period: 96 months). Figure 2 compares the time series from LOD and AAM with the ENSO indices (1962 – 2001). CorrelationsAAM Composites – ENSO events climate change (ERP - CLIVAR)“. Main focus of this research effort is on climate variability and associated variations in the Earth rotation parameters (ERP) on interannual and decadal time scales. Under this premise, variations in the ERPs are investigated as an independent parameter for climate indicators. Webpage Earth Rotation Portal: http://www.erdrotation.de (Project P10) centuries. On an interannual scale, variations of the AAM are excited by large scale atmospheric circulation patterns, e. g. the El Niño-Southern Oscillation (ENSO). This study focuses on the impact of ENSO on LOD and how this impact can vary for each ENSO event. The research done for this study complements the DFG funded Research Unit 584 „Earth rotation and global dynamic processes“ and its subproject „Long-term ERP time series as indicators for global climate variability and In the absence of external torques, the earth system can be regarded as a closed angular momentum conserving system. Thus, any change of angular momentum in a fluid component of the earth’s system is related to an equivalent variation in angular momentum of the solid earth. Consequently, variations in the rotation of the solid earth are reflected in the length of day (LOD). Modifications in the angular momentum are excited by local mass transport and circulation anomalies on scales from days to Calculations and plots via http://climexp.knmi.nl (Climate Explorer) source: http://www.pmel.noaa.gov/tao/elnino/nino-home.html Significance level: 99% Maxima (Minima) in red (bue) indicate the moderate (NINO3.4 >  0.9°C) and strong El Niño (La Niña) events (NINO3.4 >  1.4°C). Pearson correlation over a time period of 1962 to 2001 0.710.580.510.46LOD AAMMEININO3.4-SOI LOD SST AAM For each ENSO event LOD (annual cycle removed, thus including decadal variability, cf. Fig. 3a): correlations reveal the ENSO pattern and decadal variations in the Atlantic LOD (annual cycle and decadal variability removed, cf. Fig. 2) resembles clearly the ENSO pattern (cf. Fig. 3b) ‘82/83-El Niño: correlation pattern reveals a high oceanic contribution to the variability of LOD, while other ENSO events do not indicate a typical pattern 1981 – 2004 for LOD 1981 – 2001 for AAM Examples El Niño - La Niña phase El Niño 1982/83El Niño 1991/92 Fig. 1a Fig. 1b LOD vs. Reynolds SST Fig. 3a Filtered LOD vs. Reynolds SST Fig. 3b AAM (Fig. 2) vs. Reynolds SST Fig. 3c El Niño 1982/83 Fig. 3d El Niño 1991/92 Fig. 3e El Niño 1994/95 Fig. 3f Central equatorial Pacific: negative anomaly representing enhanced trade winds during La Niña. Subtropics: positive anomalies relating to an intensification of the subtropical jet during El Niño. Specific El Niño anomaly to climate average The anomaly pattern of ‘82/83 event reveals a similar distribution to the all-El Niño anomaly, indicating that both atmosphere and ocean (cf. Fig. 3d) have a significant influence. El Niño ‘91/92 is characterized by a well corresponding atmospheric pattern. Considering the missing signals in the SST-LOD correlation (cf. Fig. 3e) the assumption is that atmospheric anomalies play a more dominant role during this event. El Niño ’94/95 differs completely: As well the correlation between SST field and LOD show arbitrary signals (cf. Fig. 3f). Consequently, a clear diagnosis of the LOD influencing variability is not possible. El Niño 1994/95 (NINO3.4 exceeds at least for 5 months  0.5°C) - Fig. 2 -