Search for: All records

Abstract Climate models suffer from longstanding precipitation biases, much of which has been attributed to their atmospheric component owing to unrealistic parameterizations. Here we investigate precipitation biases in 37 Atmospheric Model Intercomparison Project Phase 6 (AMIP6) models, focusing on the Indo‐Pacific region during boreal summer. These models remain plagued by considerable precipitation biases, especially over regions of strong precipitation. In particular, 22 models overestimate the Asian‐Pacific monsoon precipitation, while 28 models underestimate the southern Indian Ocean Intertropical Convergence Zone precipitation. The inter‐model spread in summer precipitation is decomposed into Empirical Orthogonal Functions (EOFs). The leading EOF mode features an anomalous anticyclone circulation spanning the Indo‐northwest Pacific oceans, which we show is energized by barotropic conversion from the confluence of the background monsoonal westerlies and trade‐wind easterlies. Our results suggest precipitation biases in atmospheric models, though caused by unrealistic parameterizations, are organized by dynamical feedbacks of the mean flow. 

Abstract Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to various societal applications. Here we evaluate seasonal forecasts of three climate variables, vapor pressure deficit (VPD), temperature, and precipitation, from operational dynamical models over the major cropland areas of South America; analyze their predictability from global and local circulation patterns, such as El Niño–Southern Oscillation (ENSO); and attribute the source of prediction errors. We show that the European Centre for Medium-Range Weather Forecasts (ECMWF) model has the highest quality among the models evaluated. Forecasts of VPD and temperature have better agreement with observations (average Pearson correlation of 0.65 and 0.70, respectively, among all months for 1-month-lead predictions from the ECMWF) than those of precipitation (0.40). Forecasts degrade with increasing lead times, and the degradation is due to the following reasons: 1) the failure of capturing local circulation patterns and capturing the linkages between the patterns and local climate; and 2) the overestimation of ENSO’s influence on regions not affected by ENSO. For regions affected by ENSO, forecasts of the three climate variables as well as their extremes are well predicted up to 6 months ahead, providing valuable lead time for risk preparedness and management. The results provide useful information for further development of dynamical models and for those who use seasonal climate forecasts for planning and management. Significance Statement Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to their applications. This study evaluated the quality of monthly forecasts of three important climate variables that are critical to agricultural management, risk assessment, and natural hazards warning. The findings provide useful information for those who use seasonal climate forecasts for planning and management. This study also analyzed the predictability of the climate variables and the attribution of prediction errors and thus provides insights for understanding models’ varying performance and for future improvement of seasonal climate forecasts from dynamical models. 

Abstract During boreal winter (December–February), the South American monsoon system (SAMS) reaches its annual maximum when upper‐tropospheric westerly winds prevail over the equatorial Atlantic. Atmospheric dynamic model simulations suggest that Rossby waves generated over South America can propagate to and potentially influence weather patterns in the Northern Hemisphere (NH). However, observational evidence has been absent previously. Here we focus on southeastern South American (SESA) precipitation anomalies, which can characterize intraseasonal rainfall variability of the SAMS. Since tropical “westerly duct” and convective heating are important factors for cross‐equatorial propagation of Rossby wave (CEPRW), we identify two groups of events based on the two factors. By comparing the events associated with both SESA rainfall and tropical westerlies to the events associated with tropical westerlies only, we find that an anomalous Rossby wave train is triggered by precipitation anomalies over SESA, propagates in the southwest–northeast direction, and subsequently crosses the equator. Over a period of 4 days, near‐surface temperature over northwestern Africa and western Europe becomes warmer, accompanied by increased surface downward longwave radiation and precipitable water. The equatorward propagating Eliassen–Palm flux anomalies originated from SESA support the evidence of CEPRW. Simulations using a time‐dependent linear barotropic model forced by prescribed divergence anomalies over SESA further confirm that SESA rainfall can influence the NH weather patterns through CEPRW. Knowledge of this study will help us better understand and model interhemispheric teleconnections over the American–Atlantic–African/European sector. 

Abstract Atlantic Niños dominate the equatorial Atlantic variability during boreal summer (June–August). The coupled ocean‐atmosphere processes associated with Atlantic Niños have been extensively documented. However, the role of atmospheric convectively coupled Kelvin waves (CCKWs), which are uncorrelated to those previously identified processes, in triggering Atlantic Niños has been unclear. Here we identify CCKWs using Wheeler‐Kiladis filtering based on 10°S–10°N averaged daily outgoing longwave radiation. CCKWs propagate eastward from South America and induce surface zonal wind anomalies over the equatorial Atlantic Ocean. Strong anomalous CCKWs during spring (March–May) and their associated surface westerly wind anomalies can trigger downwelling oceanic Kelvin waves that change the east–west slope of the thermocline, consequently leading to Atlantic Niño. A causal effect network reveals that interannual sea surface temperature (SST) anomalies in the Atlantic Niño Index area and CCKWs, both in spring, are uncorrelated, but both appear to influence SST anomalies over the Atlantic Niño Index area in summer. The CCKWs are also uncorrelated to other coupled ocean‐atmosphere sources, such as El Niño–Southern Oscillation and Atlantic Meridional Mode. Among a total of 15 Atlantic Niño/Niña events identified for the period of 1980–2017, two‐thirds of the events are linked to CCKWs. In particular, three Atlantic Niña events (1982, 1994, and 2005) are mainly triggered by CCKWs, under unfavorable SST preconditions. Thus, CCKWs in spring, due to atmospheric internal variability, provide another mechanism for triggering Atlantic Niños and probably weaken their predictability.