Abstract

The current work investigated the interdecadal changes in the leading empirical orthogonal function (EOF) pattern of the interannual variation in spring [March–May (MAM)] snow-cover extent (SCE) over the Tibetan Plateau (TP) (SSC_TP). The leading EOF pattern of the SSC_TP is transformed from an east to west dipole pattern during the period 1970–89 (P1) to a monopole structure during the period 1991–2020 (P2). Observational analysis shows that during P1, the negative Antarctic Oscillation (AAO) (−AAO) is associated with low-level cross-equator southeasterly anomalies across the Bay of Bengal and transports more water vapor to the eastern TP. Moreover, at a high level, anomalous northerly winds accompanied by an anomalous sinking motion dominate the western TP, favoring an east-wet–west-dry dipole pattern of SSC_TP. Further analysis shows that the −AAO induces anomalous divergence over the Antarctic, which contributes to the formation of a Rossby wave source (RWS). This RWS is related to a northeastward-propagating atmospheric wave train that crosses the equator and contributes to the SSC_TP variation during P1. In contrast, in P2, the Arctic Oscillation (AO) is associated with a barotropic atmospheric wave train originating from southern Greenland, moving across the North Atlantic Ocean and North Africa and reaching the TP. This wave train results in significant positive vorticity and ascending airflow above the TP and favors a monopole pattern of the SSC_TP. Further analysis shows that the AO can induce divergence anomalies over southeastern Greenland and RWS anomalies there. This RWS induces an atmospheric wave train that propagates eastward and reaches the TP during P2. The above mechanisms have been supported by the results of numerical experiments performed using the linear baroclinic model.

Abstract

Central Africa (CA) is identified as a location of a large positive trend of the occurrence of heat waves (HWs) during 1979–2016, appearing to result mostly from a regime shift around the year 2000. Therefore, we study the evolution of synoptic features associated with the occurrence of HW events in CA. It is found that the HW-related circulation is typically characterized by an anomalous convergence in the upper troposphere but there are important differences for HW events occurring in the south region of CA (CA_S) versus the north region (CA_N). For the occurrence of the HW events in CA_S, the anomalous subsidence associated with upper troposphere anomalous convergence is the dominant factor for their occurrence and magnitude: the strong subsidence leads to warming through greater solar insolation. The HW events in CA_S are also accompanied by an anomalous surface anticyclone in the north with anomalous northerly flow transporting heat into the CA_S region. In contrast, although the HW events in CA_N are also associated with upper troposphere anomalous convergence, the intensity of the convergence is weak with small solar insolation. Instead, the anomalous warm advection is the main factor for determining the magnitude of the HW events in CA_N, induced by the prevailing northerly winds acting on the anomalous temperature gradient. Thus, the synoptic features associated with HW events in the CA_N and CA_S are quite different despite their nearby locations. The discovered dominant factors for the HW events in CA can be used to improve the forecast skill.

Abstract

In this study, temporal and spatial variability of ice cover in the Great Lakes are investigated using historical satellite measurements from 1973 to 2010. The seasonal cycle of ice cover was constructed for all the lakes, including Lake St. Clair. A unique feature found in the seasonal cycle is that the standard deviations (i.e., variability) of ice cover are larger than the climatological means for each lake. This indicates that Great Lakes ice cover experiences large variability in response to predominant natural climate forcing and has poor predictability. Spectral analysis shows that lake ice has both quasi-decadal and interannual periodicities of ~8 and ~4 yr. There was a significant downward trend in ice coverage from 1973 to the present for all of the lakes, with Lake Ontario having the largest, and Lakes Erie and St. Clair having the smallest. The translated total loss in lake ice over the entire 38-yr record varies from 37% in Lake St. Clair (least) to 88% in Lake Ontario (most). The total loss for overall Great Lakes ice coverage is 71%, while Lake Superior places second with a 79% loss. An empirical orthogonal function analysis indicates that a major response of ice cover to atmospheric forcing is in phase in all six lakes, accounting for 80.8% of the total variance. The second mode shows an out-of-phase spatial variability between the upper and lower lakes, accounting for 10.7% of the total variance. The regression of the first EOF-mode time series to sea level pressure, surface air temperature, and surface wind shows that lake ice mainly responds to the combined Arctic Oscillation and El Niño–Southern Oscillation patterns.

Abstract

An assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Niño–Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible causes of model biases generated from simulated large-scale climate conditions, are documented in the paper. The model experiments are carried out by the Hurricane Work Group under the U.S. Climate Variability and Predictability Research Program (CLIVAR) using five global climate models (GCMs) with a total of 16 ensemble members forced by the observed sea surface temperature and spanning the 28-yr period from 1982 to 2009. The results show GISS and GFDL model ensemble means best simulate the interannual variability of TCs, and the multimodel ensemble mean (MME) follows. Also, the MME has the closest climate mean annual number of WNP TCs and the smallest root-mean-square error to the observation.

Most GCMs can simulate the interannual variability of WNP TCs well, with stronger TC activities during two types of El Niño—namely, eastern Pacific (EP) and central Pacific (CP) El Niño—and weaker activity during La Niña. However, none of the models capture the differences in TC activity between EP and CP El Niño as are shown in observations. The inability of models to distinguish the differences in TC activities between the two types of El Niño events may be due to the bias of the models in response to the shift of tropical heating associated with CP El Niño.