References (1) Blanford, HF (1884) Proc. Roy. Soc. London 37. // (2) Becker, BD, JM Slingo, L Ferranti, F Molteni (2001) Mausam 52. // (3) Bamzai, AS & J Shukla (1999) J Climate 12. // (4) Robock, A, MQ Mu, K Vinnikov, D Robinson (2003) J. Geophys. Res // (5) Fasullo, J (2004) J. Climate 17. // (6) Turner, AG, PM Inness, JM Slingo (2005) QJRMS 131. // (7) Corti, S, F Molteni, C Brankovic (1999) QJRMS 126. Snow-monsoon behaviour in the year HadCM3 control run and interactions with remote ENSO forcing Dr. Andy Turner and Prof. Julia Slingo NCAS-Climate, University of Reading  HadCM3 features weak snow-monsoon correlations over whole integration, possibly due to poor monsoon-ENSO links.  EOF1 suggests that negative correlations existing in each region of interest must compete to force the monsoon.  West Eurasian and local snow forcing act on the monsoon by changes to circulation or meridional temperature gradient.  AGCM experiments with climatological SST are required to test competing snow forcing from each region. Summary Fig. 3 shows correlations between spring snow amount (Feb-Mar-Apr) in HadCM3 and Indian summer monsoon indices: Indian Rainfall (AIR) and Webster-Yang zonal windshear index (DMI). Partial correlations with respect to spring Niño-3 conditions are also shown. Walker Institute for Climate System Research Deficiencies in the HadCM3 monsoon- ENSO teleconnection have implications for snow-monsoon-ENSO relationships: Model UK Met Office coupled model HadCM year control run. Monthly data made available by the BADC. Atmospheric resolution 3.75 ° x 2.5 ° x L19. Summer teleconnection is too weak and mistimed in a 100-year L30 integration of HadCM3 (6). Fig. 2 shows the same problem at L19 resolution for 1050-years. Wrong signed relationship between previous winter SST and monsoon rainfall suggests concurrent relationship between snow and ENSO may also be erroneous. EOF1 of interannual spring snow amount in Fig. 1 explains ~30% variance and suggests opposite signals in western Eurasia and Himalaya / Tibetan Plateau  snow in west Eurasia and the Himalaya may act on the Indian monsoon using competing mechanisms. Ignoring other factors, Indian rainfall should respond to local snow forcing (Himalaya / Tibetan Plateau) via Blanford hypothesis (2,7). Monsoon dynamics are negatively influenced by snow forcing from Western Eurasia (2,7). ENSO forcing may help determine which region of snow impacts most strongly on the subsequent monsoon, although this is difficult to test in this model. Small but significant negative correlations between Himalayan snow and monsoon rainfall (these disintegrate when the effects of ENSO are removed). Negative correlations between west Eurasian snow and monsoon dynamics (these strengthen when the effects of ENSO are removed). Despite the low impact of local snow on the monsoon in the absence of ENSO over the whole integration, some individual years may be used to generate composites. Composite differences of various fields (Fig. 4) are constructed from heavy snow followed by weak monsoon years minus light snow followed by strong monsoon years based on spring (FMA) indices of snow amount in W Eurasia ( ° E, ° N, left) and Himalaya/Tibetan Plateau ( ° E, ° N, right) during neutral ENSO conditions in MAM. Snow anomalies over W/N Eurasia are of opposite sign to those over Himalaya. SAT and Z500 responses strongly depend on location of snow anomaly. Greater weakening of upper level monsoon flow when forced by remote snow, consistent with shift in jet. 850hPa wind and precip respond more readily to local snow forcing. Snow-monsoon links in HadCM3 Working hypothesis Background Snow composite differences ENSO-monsoon relationship Left: Difference composites using Himalayan snow index and Indian rainfall. Right: using west Eurasia snow index and DMI. Himalayan snow cover in previous winter/spring has long been regarded as a predictor for the Indian summer monsoon (1), heavy snow being followed by reduced rainfall. Various recent studies have since noted inconsistencies in the region and mechanism of the snow teleconnection (e.g., western or northern Eurasia: 2,3,4; Himalaya / Tibetan Plateau: 5).  Mechanisms are dependent on the region of forcing, either via albedo / soil moisture / surface temperature or circulation. Here we determine the predominant mechanisms linking snow forcing with the Indian summer monsoon in a long control integration of the Hadley Centre coupled model. Lag correlations between Niño-3 SST and JJAS Indian rainfall in observations ( ) and HadCM3. MAM snow (kg/m 2 ) Apr 1.5m temp ( ° C) & Z500 (m) May 200hPa wind (m/s) JJA 850hPa wind (m/s) & rainfall (mm/d)