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To evaluate the atmosphere–ocean coupled data assimilation system developed at the Meteorological Research Institute, the lead-lag relation between the intraseasonal variations (with a time scale of 20–100 days) in precipitation and sea surface temperature (SST) is examined in the tropics. It is shown that the relationship over the tropical western Pacific in the coupled reanalysis experiment (CDA) follows the observed relationship more closely than that in the uncoupled reanalysis experiment (UCPL). However, the lead-lag correlations with the observed SST are almost identical between precipitations in CDA and UCPL, indicating that the atmospheric component is strongly constrained by atmospheric observations and hardly affected by the SSTs as boundary conditions. Better representation of the SST–precipitation relationship in CDA is, thus, mostly due to the SST variation modified by the model physics. Comparison with additional reanalysis experiments using coupled and uncoupled systems that assimilate only in-situ observations without satellite observations suggests that the coupled model's physics complements the relatively weak observation constraints and reduces the degradation of the SST–precipitation relationship. Additional analysis for CDA suggests that the warming-to-cooling (cooling-to-warming) transition of the surface net flux, which is in phase with precipitation, is delayed from the positive (negative) peak of SST due to downward heat propagation in the ocean. Comparison of the oceanic near-surface temperature field with observation data indicates that the downward propagation of heat signals is too fast in CDA, resulting in smaller lags of transitions of the net heat flux and precipitation behind SST peaks.

Climate models in the past decades have been developed to such an extent to include atmospheric chemistry as part of their climate simulations. This is necessary for providing policy-makers and other stakeholders with reliable atmospheric projections as well as information about changes in chemical species as a consequence of climate change. The regional climate model (RCM), RegCM4 is a community model which contains only one gas-phase mechanism module (CBM-Z) that includes the formation, deposition, and transport of a number of volatile organic compounds (VOCs). In this paper, the CB6-C, a new gas-phase mechanism module, is combined with RegCM4 to produce a larger suite of VOCs and chemical mechanisms for important anthropogenic and biogenic species, most notably benzene, terpenes, acetylene and their corresponding oxidation products. In order to evaluate this new module, climatologies of the CB6-C and CBM-Z simulations are compared to gridded and station data. The results reveal the two schemes to be similar with some improvement of surface carbon monoxide and tropospheric ozone in the CB6-C. However, organic products were found to be under-predicted for both schemes, suggesting the need of more development in the implementation of atmospheric chemistry in RegCM4. Despite its limitations, the input conditions (emissions and boundary conditions) are easy to modify, making the new gas-phase scheme an important advancement in the modelling of atmospheric chemistry within a RCM, as it provides a pathway for new research that may eventually help health studies.

The “Western Tibetan Vortex” (WTV)—also termed the Karakoram Vortex—dominates the middle-to-lower troposphere and the near-surface air temperature variability above the western Tibetan Plateau (TP). Here, we explore the thermodynamic mechanisms through which the WTV modulates air temperature over the western TP by diagnosing the three major terms of the thermodynamic energy equation—adiabatic heating, horizontal temperature advection, and diabatic heating—that maintain the atmospheric thermal balance. We composite these major terms to examine the differences between anti-cyclonic and cyclonic WTV events. Our theoretical approach demonstrates that adiabatic sinking-compression (rising-expansion) provides the overwhelming control on both the middle-to-lower tropospheric and lower stratospheric temperature increases (decreases) under anti-cyclonic (cyclonic) WTV conditions over the western TP high mountain area in all four seasons. This also explains the mechanisms behind the anomalous temperature “dipole” found between the mid-lower troposphere and lower stratosphere when the WTV was initially identified. Spatially, adiabatic heating effects are centred on the central western TP in summer and the south slope centring at 70°–80°E of the TP in other seasons. The other two terms, horizontal temperature advection and diabatic heating, have localized importance over the edges of the western TP. In a case study over the Karakoram area, we further demonstrate that adiabatic heating (rising-expanding-cooling/sinking-compressing-warming) is the dominant thermodynamic process controlling Karakoram air temperatures under WTV variability, except for at the very near surface in autumn and winter. Our analysis methods can be applied to investigate the thermodynamic processes of other atmospheric circulation systems or climate variability modes.

The efficient and accurate selection of primary drought-driving factors as the independent variables of drought prediction model is critical in improving drought prediction accuracy. In this study, a novel feature selection method based on information changing rate and conditional mutual information (ICR-CMIFS) was proposed and evaluated by the comparison with other feature selection methods from feature selection, simulation, and classification aspects; two artificial intelligence drought prediction models, which treated the factors selected by ICR-CMIFS, correlation analysis (CA) and mutual information maximum (MIM) respectively as independent variables and standardized precipitation evapotranspiration index (SPEI) in 3/6/12-month time scales as dependent variables, were established; the superiority of ICR-CMIFS over CA and MIM methods in the selection of primary climatic drought-driving factors in Yunnan-Guizhou Plateau (YGP) was tested by the performance of the two models. The results revealed: the ICR-CMIFS was superior to the other feature selection methods; both artificial intelligence drought prediction models with the independent variables selected by ICR-CMIFS performed better in terms of correlation coefficient, Nash–Sutcliffe coefficient, root-mean square error and model computing time than by the MIM and CA methods. The outputs can provide an innovative approach in selecting primary drought-driving factors and improving drought prediction accuracy.

In this study we present a diagnostic model for the large-scale tropical circulation (vertical motion) based on the moist static energy equation for first baroclinic mode anomalies (MSEB model). The aim of this model is to provide a basis for conceptual understanding of the drivers of the large-scale tropical circulation changes or variations as they are observed or simulated in Coupled Model Inter-comparison Project (CMIP) models. The MSEB model is based on previous studies relating vertical motion in the tropics to the driving forces of the tropospheric column heating rate, advection of moisture and heat, and the moist stability of the air columns scaled by the first baroclinic mode. We apply and evaluate the skill of this model on the basis of observations (reanalysis) and CMIP model simulations of the large-scale tropical vertical motion. The model is capable of diagnosing the large-scale pattern of vertical motion of the mean state, annual cycle, interannual variability, model-to-model variations and in warmer climates of climate change scenarios; it has spatial correlations of 0.6–0.8 and nearly unbiased amplitudes for the whole tropics (30° S–30° N). The skills are generally better over oceans at large scales and worse over land regions. For the interannual variation of zonally anomalous and zonal mean circulation in tropical Pacific region, it has temporal correlations ~ 0.8. The model also tends to have an upward motion bias at higher latitudes, but still has good correlations in temporal and spatial variations even at the higher latitudes. It is further illustrated how the MSEB model sensitivities can be used to determine the mechanisms in the models that are responsible for the mean state, seasonal cycle and interannual variability such as El Nino. The model clearly illustrates that the seasonal cycle in the circulation is driven by the incoming solar radiation and that the El Nino shift in the Walker circulation results mainly from the sea-surface temperature changes. Overall, the model provides a powerful diagnostic tool to understand tropical circulation change on larger and longer (> month) time scales.

Glacial–interglacial cycles constitute large natural variations in Earth’s climate. The Mid-Pleistocene Transition (MPT) marks a shift of the dominant periodicity of these climate cycles from $$\sim 40$$ to $$\sim 100$$  kyr. Recently, it has been suggested that this shift resulted from a gradual increase in the internal period (or equivalently, a decrease in the natural frequency) of the system. As a result, the system would then have locked to ever higher multiples of the external forcing period. We find that the internal period is sensitive to the strength of positive feedbacks in the climate system. Using a carbon cycle model in which feedbacks between calcifier populations and ocean alkalinity mediate atmospheric CO $$_2,$$ we simulate stepwise periodicity changes similar to the MPT through such a mechanism. Due to the internal dynamics of the system, the periodicity shift occurs up to millions of years after the change in the feedback strength is imposed. This suggests that the cause for the MPT may have occurred a significant time before the observed periodicity shift.

A statistical study was made of the temporal trend in extreme temperatures in the region of Extremadura (Spain) during the period 1981–2015 using a Regional Climate Model. For this purpose, a Weather Research and Forecasting (WRF) Regional Climate Model extreme temperature dataset was obtained. This dataset was then subjected to a statistical study using a Bayesian hierarchical spatio-temporal model with a Generalized Extreme Value (GEV) parametrization of the extreme data. The Bayesian model was implemented in a Markov chain Monte Carlo framework that allows the posterior distribution of the parameters that intervene in the model to be estimated. The role of the altitude dependence of the temperature was considered in the proposed model. The results for the spatial-trend parameter lend confidence to the model since they are consistent with the dry adiabatic gradient. Furthermore, the statistical model showed a slight negative trend for the location parameter. This unexpected result may be due to the internal and modeling uncertainties in the WRF model. The shape parameter was negative, meaning that there is an upper bound for extreme temperatures in the model.

The role of topography in the diurnal cycle of precipitation is analyzed during the propagation of a Madden–Julian Oscillation (MJO) event over the Islands of the Maritime Continent using cloud-permitting simulations. The control simulation (CTL) using realistic topography captures the timing, magnitude, and location of the observed diurnal cycle of precipitation. The idealized simulation (FLAT) without topography delays the arrival of peak precipitation by about an hour compared to CTL. The magnitude of area-averaged precipitation remains unchanged in both simulations over areas with altitude < 500 m. The largest difference is found over areas with relatively high topography (> 1000 m), where diurnal rainfall was significantly reduced in FLAT. The comparison between both simulations from a moisture budget analysis shows that 62% of the reduction in precipitation in FLAT is associated with a reduction in vertical advection (VADV), and 31% with a reduction in horizontal advection (HADV) of moisture. Furthermore, the reduction in VADV was equally contributed by the planetary boundary layer (PBL) and the free troposphere, whereas the reduction in HADV was mostly (~ 80%) confined within the PBL. The moisture budget analysis of the MJO event shows that the changes in background winds significantly impact precipitation over lower topography areas. These results also indicate that the changes in moisture advection above PBL are important to better understand and monitor the moisture budget in the troposphere over land in the MC.

This paper is focused on the West African anomalous precipitation response to an Atlantic Equatorial mode whose origin, development and damping resembles the observed one during the last decades of the XXth century. In the framework of the AMMA-EU project, this paper analyses the atmospheric response to the Equatorial mode using a multimodel approach with an ensemble of integrations from 4 AGCMs under a time varying Equatorial SST mode. The Guinean Gulf precipitation, which together with the Sahelian mode accounts for most of the summer West African rainfall variability, is highly coupled to this Equatorial Atlantic SST mode or Atlantic Niño. In a previous study, done with the same models under 1958–1997 observed prescribed SSTs, most of the models identify the Equatorial Atlantic SST mode as the one most related to the Guinean Gulf precipitation. The models response to the positive phase of equatorial Atlantic mode (warm SSTs) depicts a direct impact in the equatorial Atlantic, leading to a decrease of the local surface temperature gradient, weakening the West African Monsoon flow and the surface convergence over the Sahel.