A recent study categorized the Madden–Julian oscillation (MJO) during boreal winter season into four types called stand, jump, slow, and fast MJO. This study focuses on the stand and jump MJO. Based on whether their convection penetrates the Maritime Continent (MC), stand and jump MJOs are seen as non-penetrating (NP) MJOs, while the other two are seen as eastward-penetrating (EP) MJOs. Results reveal the relative roles of the westward-propagating wave (WPW), as well as the QBO and ENSO, in limiting MJO propagation. Lack of the premoistening over the southern sea surface of the MC stops NP MJO from penetrating the MC. The active convection of the WPWs hinders the descending branch of the NP MJO circulation and therefore leads to the insufficient meridional advective moistening over the southern sea surface of the MC. The independent convection over the Pacific for jump MJOs is influenced by a combined effect of the QBO and ENSO. The tropopause instability induced by the MJO is found to significantly decouple from its convection over the Pacific in westerly QBO (QBOW) winters more than in easterly QBO (QBOE) winters. For jump MJOs, the independent convection over the central Pacific comes from local WPWs whose amplification and further development into deep convection are correlated to jump the MJOs’ decoupled tropopause instability. For stand MJOs, however, the seasonal-mean La Niña–like cool SST anomalies weaken the WPW activity over the central Pacific and confine WPWs within the western Pacific. Therefore, the decoupled tropopause instability of stand MJOs is out phase of WPWs and fails to induce an independent convection over the central Pacific.

The quasi‐biennial oscillation (QBO) impacts the Madden‐Julian Oscillation (MJO) activity with a stronger MJO in QBO easterly (QBOE) than QBO westerly (QBOW) winters. However, this relationship is poorly represented in the current generation climate models. For the first time, this paper applies a stratospheric zonal‐mean nudging in a subseasonal prediction system to capture it. Two strong MJO cases in a QBO‐neutral winter are investigated. The QBO temperature and zonal wind anomalies are added separately as well as together to the stratosphere using nudging in MJO case hindcast. Only by nudging the QBO temperature anomalies while leaving the zonal wind free, can the prediction system capture the observed QBO‐MJO connection. The tropopause instability is found positively correlated to the MJO amplitude, but it cannot fully explain the captured connection. The free‐evolving zonal wind anomalies in the stratosphere due to the nudged QBO temperature are crucial for the captured connection.

Past and projected changes in global hydroclimate in Earth system models have been examined. The Budyko framework that relates the partitioning of precipitation into evaporation to a location’s aridity has been modified to account for the effect of interannual terrestrial water storage and compared to traditional methods. The new formulation better fits climate model data over most of the globe. Old and new formulations are used to quantify changes in the spatial patterns of hydroclimate based locally on year-to-year variations water and energy cycle variables. Focus is on multi-model median responses to changing climate. The changes in hydroclimate from preindustrial to recent historical (1965-2014) conditions often have different patterns and characteristics than changes due only to increasing CO ₂ . For simulations with gradually increasing CO ₂ , differing model treatments of vegetation are found specifically to have categorically different impacts on hydroclimate, particularly altering the relationship between aridity and the fraction of precipitation contributing to evaporation in models that predict vegetation changes. Models that predict vegetation phenology have consistently different responses to increasing CO ₂ than models that do not. Dynamic vegetation models show more widespread but less consistent differences than other models, perhaps reflecting their less mature state. Nevertheless, there is clearly sensitivity to vegetation that illustrates the importance of including the representation of biospheric shifts in Earth system models.

Subseasonal-to-seasonal (S2S) forecasts are critically important for anticipating socio-economic impacts but remain a significant scientific challenge. Reliable forecasts beyond a two-week timeframe require both accurate initial conditions and realistic representations of coupled Earth system processes. In particular, Earth’s land surface provides a key source of predictability for S2S predictions through land-atmosphere interactions. Here, we present a 25-year global 60-km simulation with the Model for Prediction Across Scales (MPAS) coupled with Noah-MP land surface model, with ERA-5 sea surface temperature and sea ice boundary conditions. This study evaluates the model’s climatology, systematic biases, and its ability to represent land-atmosphere interactions, all of which are necessary for future S2S applications. MPAS-NoahMP well captures annual surface temperature in the tropics and midlatitudes, while winter temperature in North America and Asia is sensitive to sea ice thickness and snowpack thermal properties. The global precipitation pattern is also well captured by MPAS-NoahMP, with slight dry biases in the tropics over the Eastern Pacific, Amazon, and Africa Congo, and wet biases in the Indian ocean and Maritime continent. The model accurately simulates spatial patterns of land-atmosphere coupling indices and hotspots, including winter snow-temperature coupling and summer soil moisture-latent heat flux coupling. Furthermore, MPAS-NoahMP reasonably captures the major modes of tropical variability, including the El Nino and Southern Oscillation and the Madden Julian Oscillation. This study is the first to establish a long-term MPAS-NoahMP reference dataset for understanding model biases and assessing land-atmosphere interactions, providing a foundation for extending the coupled MPAS-NoahMP system to future S2S applications.