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The European Centre for Medium-Range Weather Forecasts' (ECMWF's) next-generation reanalysis ERA5 provides many improvements, but it also confronts the community with a “big data” challenge. Data storage requirements for ERA5 increase by a factor of ∼80 compared with the ERA-Interim reanalysis, introduced a decade ago. Considering the significant increase in resources required for working with the new ERA5 data set, it is important to assess its impact on Lagrangian transport simulations. To quantify the differences between transport simulations using ERA5 and ERA-Interim data, we analyzed comprehensive global sets of 10-day forward trajectories for the free troposphere and the stratosphere for the year 2017. The new ERA5 data have a considerable impact on the simulations. Spatial transport deviations between ERA5 and ERA-Interim trajectories are up to an order of magnitude larger than those caused by parameterized diffusion and subgrid-scale wind fluctuations after 1 day and still up to a factor of 2–3 larger after 10 days. Depending on the height range, the spatial differences between the trajectories map into deviations as large as 3 K in temperature, 30 % in specific humidity, 1.8 % in potential temperature, and 50 % in potential vorticity after 1 day. Part of the differences between ERA5 and ERA-Interim is attributed to the better spatial and temporal resolution of the ERA5 reanalysis, which allows for a better representation of convective updrafts, gravity waves, tropical cyclones, and other meso- to synoptic-scale features of the atmosphere. Another important finding is that ERA5 trajectories exhibit significantly improved conservation of potential temperature in the stratosphere, pointing to an improved consistency of ECMWF's forecast model and observations that leads to smaller data assimilation increments. We conducted a number of downsampling experiments with the ERA5 data, in which we reduced the numbers of meteorological time steps, vertical levels, and horizontal grid points. Significant differences remain present in the transport simulations, if we downsample the ERA5 data to a resolution similar to ERA-Interim. This points to substantial changes of the forecast model, observations, and assimilation system of ERA5 in addition to improved resolution. A comparison of two Lagrangian trajectory models allowed us to assess the readiness of the codes and workflows to handle the comprehensive ERA5 data and to demonstrate the consistency of the simulation results. Our results will help to guide future Lagrangian transport studies attempting to navigate the increased computational complexity and leverage the considerable benefits and improvements of ECMWF's new ERA5 data set.

Despite the importance of precipitation and moisture transport over the Tibetan Plateau for glacier mass balance, river runoff and local ecology, changes in these quantities remain highly uncertain and poorly understood. Here we use observational data and model simulations to explore the close relationship between summer rainfall variability over the southwestern Tibetan Plateau (SWTP) and that over central-eastern India (CEI), which exists despite the separation of these two regions by the Himalayas. We show that this relationship is maintained primarily by ‘up-and-over’ moisture transport, in which hydrometeors and moisture are lifted by convective storms over CEI and the Himalayan foothills and then swept over the SWTP by the mid-tropospheric circulation, rather than by upslope flow over the Himalayas. Sensitivity simulations confirm the importance of up-and-over transport at event scales, and an objective storm classification indicates that this pathway accounts for approximately half of total summer rainfall over the SWTP. While precipitation over the Tibetan Plateau is a vital resource for glacier mass balance, river runoff and local ecology, the controlling mechanisms are poorly understood. Here, the authors combine observations and simulations and show that convective storms over India sweep moisture up and over the plateau.

An area-weighted k-means clustering method based on pattern correlations is proposed and used to explore the relationship between the Siberian High (SH) and Arctic Oscillation (AO) during the winter months (December-January-February) of 1948-2014. Five regimes are identified. Four of these five regimes (comprising 171 of 201 months) show a negative correlation between the SH and AO indices, while the last regime (30 months) shows a positive correlation. The location of the SH shifts southward into China under two of the four negative-correlation regimes (117 months), with pressure variations over the center of activity for the SH opposite to pressure variations over the climatological center of the SH (which is used to define the SH index). Adjusting the SH index to account for these spatial shifts suggests positive rather than negative correlations between major variations in the SH and AO under these regimes. Under one of the two remaining negative-correlation regimes, pressure anomalies are weak over the Arctic Ocean. In total, only one regime comprising 21 of 201 months strictly obeys the negative correlation between the SH and AO reported by previous studies. The climate regime characterized by an intensified SH is associated with a greater frequency of cold surges over northern and southeastern China, and the weakening of the East Asian winter monsoon during the 1980s was accompanied by a sharp reduction in the occurrence of this regime.

Wintertime precipitation, especially snowstorms, significantly impacts people's lives. However, the current forecast skill of wintertime precipitation is still low. Based on data augmentation (DA) and deep learning, we propose a DABU‐Net which improves the Global Forecast System wintertime precipitation forecast over southeastern China. We build three independent models for the forecast lead times of 24, 48, and 72 hr, respectively. After using DABU‐Net, the mean Root Mean Squared Errors (RMSEs) of the wintertime precipitation at the three lead times are reduced by 19.08%, 25.00%, and 22.37%, respectively. The threat scores (TS) are all significantly increased at the thresholds of 1, 5, 10, 15, and 20 mm day−1 for the three lead times. During heavy precipitation days, the RMSEs are decreased by 14% and TS are increased by 7% at the lead times within 48 hr. Therefore, combining DA and deep learning has great prospects in precipitation forecasting. Plain Language Summary In this paper, we propose a deep learning‐based method to improve the forecast performance of Global Forecast System wintertime precipitation over southeastern China. Due to the imbalanced distribution of precipitation data, we use data from the three other seasons as an augmented data set for wintertime precipitation to train the deep neural network. The results show that the method can reduce the Root Mean Squared Error and improve the TS, a metric of precipitation forecast performance, of the precipitation. In particular, TS at the threshold of 20 mm day−1 are increased by 69.23%, 90.00%, and 100.00% at lead times of 24, 48, and 72 hr. The proposed method performs well during heavy precipitation days at lead times within 48 hr. Combining data augmentation with deep learning provides a successful approach to predicting precipitation. Key Points A deep learning model based on data augmentation (DA) is proposed to improve the Global Forecast System wintertime precipitation forecast The deep learning model improves the heavy precipitation forecast at lead times within 48 hr DA plays a critical role in the heavy precipitation forecast

The influence of temperature on precipitation in China is investigated from two aspects of the atmospheric water cycle: available water vapor and atmospheric instability. Daily observations are used to analyze how rainfall intensities and its spatial distribution in mainland China depend on these two aspects. The results show that rainfall intensities, and especially rainfall extremes, increase exponentially with available water vapor. The efficiency of water vapor conversion to rainfall is higher in northwestern China where water vapor is scarce than in southeastern China where water vapor is plentiful. The results also reveal a power law relationship between rainfall intensity and convective instability. The fraction of convective available potential energy (CAPE) converted to upward velocity is much larger over southeastern China than over the arid northwest. The sensitivities of precipitation to temperature-induced changes in available water vapor and atmospheric convection are thus geographically reciprocal. Specifically, while conversion of water vapor to rainfall is relatively less efficient in southeastern China, conversion of CAPE to upward kinetic energy is more efficient. By contrast, in northwestern China, water vapor is efficiently converted to rainfall but only a small fraction of CAPE is converted to upward motion. The detailed features of these relationships vary by location and season; however, the influences of atmospheric temperature on rainfall intensities and rainfall extremes are predominantly expressed through changes in available water vapor, with changes in convective instability playing a secondary role.

The tropical tropopause layer (TTL) is the transition region between the well-mixed convective troposphere and the radiatively controlled stratosphere with air masses showing chemical and dynamical properties of both regions. The representation of the TTL in meteorological reanalysis data sets is important for studying the complex interactions of circulation, convection, trace gases, clouds, and radiation. In this paper, we present the evaluation of climatological and long-term TTL temperature and tropopause characteristics in the reanalysis data sets ERA-Interim, ERA5, JRA-25, JRA-55, MERRA, MERRA-2, NCEP-NCAR (R1), and CFSR. The evaluation has been performed as part of the SPARC (Stratosphere–troposphere Processes and their Role in Climate) Reanalysis Intercomparison Project (S-RIP). The most recent atmospheric reanalysis data sets (ERA-Interim, ERA5, JRA-55, MERRA-2, and CFSR) all provide realistic representations of the major characteristics of the temperature structure within the TTL. There is good agreement between reanalysis estimates of tropical mean temperatures and radio occultation data, with relatively small cold biases for most data sets. Temperatures at the cold point and lapse rate tropopause levels, on the other hand, show warm biases in reanalyses when compared to observations. This tropopause-level warm bias is related to the vertical resolution of the reanalysis data, with the smallest bias found for data sets with the highest vertical resolution around the tropopause. Differences in the cold point temperature maximize over equatorial Africa, related to Kelvin wave activity and associated disturbances in TTL temperatures. Interannual variability in reanalysis temperatures is best constrained in the upper TTL, with larger differences at levels below the cold point. The reanalyses reproduce the temperature responses to major dynamical and radiative signals such as volcanic eruptions and the quasi-biennial oscillation (QBO). Long-term reanalysis trends in temperature in the upper TTL show good agreement with trends derived from adjusted radiosonde data sets indicating significant stratospheric cooling of around −0.5 to −1 K per decade. At 100 hPa and the cold point, most of the reanalyses suggest small but significant cooling trends of −0.3 to −0.6 K per decade that are statistically consistent with trends based on the adjusted radiosonde data sets. Advances of the reanalysis and observational systems over the last decades have led to a clear improvement in the TTL reanalysis products over time. Biases of the temperature profiles and differences in interannual variability clearly decreased in 2006, when densely sampled radio occultation data started being assimilated by the reanalyses. While there is an overall good agreement, different reanalyses offer different advantages in the TTL such as realistic profile and cold point temperature, continuous time series, or a realistic representation of signals of interannual variability. Their use in model simulations and in comparisons with climate model output should be tailored to their specific strengths and weaknesses.

We examine differences among reanalysis highcloud products in the tropics, assess the impacts of these differences on radiation budgets at the top of the atmosphere and within the tropical upper troposphere and lower stratosphere (UTLS), and discuss their possible origins in the context of the reanalysis models. We focus on the ERA5 (fifthgeneration European Centre for Medium-range Weather Forecasts – ECMWF – reanalysis), ERA-Interim (ECMWF Interim Reanalysis), JRA-55 (Japanese 55-year Reanalysis), MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2), and CFSR/CFSv2 (Climate Forecast System Reanalysis/Climate Forecast System Version 2) reanalyses. As a general rule, JRA-55 produces the smallest tropical high-cloud fractions and cloud water contents among the reanalyses, while MERRA-2 produces the largest. Accordingly, long-wave cloud radiative effects are relatively weak in JRA-55 and relatively strong in MERRA-2. Only MERRA-2 and ERA5 among the reanalyses produce tropical-mean values of outgoing long-wave radiation (OLR) close to those observed, but ERA5 tends to underestimate cloud effects, while MERRA-2 tends to overestimate variability. ERA5 also produces distributions of longwave, short-wave, and total cloud radiative effects at the top of the atmosphere that are very consistent with those observed. The other reanalyses all exhibit substantial biases in at least one of these metrics, although compensation between the long-wave and short-wave effects helps to constrain biases in the total cloud radiative effect for most reanalyses. The vertical distribution of cloud water content emerges as a key difference between ERA-Interim and other reanalyses. Whereas ERA-Interim shows a monotonic decrease of cloud water content with increasing height, the other reanalyses all produce distinct anvil layers. The latter is in better agreement with observations and yields very different profiles of radiative heating in the UTLS. For example, whereas the altitude of the level of zero net radiative heating tends to be lower in convective regions than in the rest of the tropics in ERAInterim, the opposite is true for the other four reanalyses. Differences in cloud water content also help to explain systematic differences in radiative heating in the tropical lower stratosphere among the reanalyses. We discuss several ways in which aspects of the cloud and convection schemes impact the tropical environment. Discrepancies in the vertical profiles of temperature and specific humidity in convective regions are particularly noteworthy, as these variables are directly constrained by data assimilation, are widely used, and feed back to convective behaviour through their relationships with thermodynamic stability.

Bioenergy with carbon capture and storage (BECCS) is considered to be a key technology for removing carbon dioxide from the atmosphere. However, large-scale bioenergy crop cultivation results in land cover changes and activates biophysical effects on climate, with earth’s water recycling altered and energy budget re-adjusted. Here, we use a coupled atmosphere-land model with explicit representations of high-transpiration woody (i.e., eucalypt) and low-transpiration herbaceous (i.e., switchgrass) bioenergy crops to investigate the range of impact of large-scale rainfed bioenergy crop cultivation on the global water cycle and atmospheric water recycling. We find that global land precipitation increases under BECCS scenarios, due to enhanced evapotranspiration and inland moisture advection. Despite enhanced evapotranspiration, soil moisture decreases only slightly, due to increased precipitation and reduced runoff. Our results indicate that, at the global scale, the water consumption by bioenergy crop growth would be partially compensated by atmospheric feedbacks. Thus, to support more effective climate mitigation policies, a more comprehensive assessment, including the biophysical effects of bioenergy cultivation, is highly recommended. Increased global land precipitation, due to the atmospheric feedbacks of large-scale bioenergy cultivation, may partially compensate the water consumption by such rainfed bioenergy crops at the global scale.

Stratospheric water vapor (SWV) plays important roles in the radiation budget and ozone chemistry and is a valuable tracer for understanding stratospheric transport. Meteorological reanalyses provide variables necessary for simulating this transport; however, even recent reanalyses are subject to substantial uncertainties, especially in the stratosphere. It is therefore necessary to evaluate the consistency among SWV distributions simulated using different input reanalysis products. In this study, we evaluate the representation of SWV and its variations on multiple timescales using simulations over the period 1980–2013. Our simulations are based on the Chemical Lagrangian Model of the Stratosphere (CLaMS) driven by horizontal winds and diabatic heating rates from three recent reanalyses: ERA-Interim, JRA-55 and MERRA-2. We present an intercomparison among these model results and observationally based estimates using a multiple linear regression method to study the annual cycle (AC), the quasi-biennial oscillation (QBO), and longer-term variability in monthly zonal-mean H2O mixing ratios forced by variations in the El Niño–Southern Oscillation (ENSO) and the volcanic aerosol burden. We find reasonable consistency among simulations of the distribution and variability in SWV with respect to the AC and QBO. However, the amplitudes of both signals are systematically weaker in the lower and middle stratosphere when CLaMS is driven by MERRA-2 than when it is driven by ERA-Interim or JRA-55. This difference is primarily attributable to relatively slow tropical upwelling in the lower stratosphere in simulations based on MERRA-2. Two possible contributors to the slow tropical upwelling in the lower stratosphere are suggested to be the large long-wave cloud radiative effect and the unique assimilation process in MERRA-2. The impacts of ENSO and volcanic aerosol on H2O entry variability are qualitatively consistent among the three simulations despite differences of 50 %–100 % in the magnitudes. Trends show larger discrepancies among the three simulations. CLaMS driven by ERA-Interim produces a neutral to slightly positive trend in H2O entry values over 1980–2013 (+0.01 ppmv decade−1), while both CLaMS driven by JRA-55 and CLaMS driven by MERRA-2 produce negative trends but with significantly different magnitudes (−0.22 and −0.08 ppmv decade−1, respectively).

Climate models have long‐standing difficulties simulating the South Pacific Convergence Zone (SPCZ) and its variability. For example, the default Zhang‐McFarlane (ZM) convection scheme in the Community Atmosphere Model version 5 (CAM5) produces too much light precipitation and too little heavy precipitation in the SPCZ, with this bias toward light precipitation even more pronounced in the SPCZ than in the tropics as a whole. Here, we show that implementing a recently developed convection scheme in the CAM5 yields significant improvements in the simulated SPCZ during austral summer and discuss the reasons behind these improvements. In addition to intensifying both mean rainfall and its variability in the SPCZ, the new scheme produces a larger heavy rainfall fraction that is more consistent with observations and state‐of‐the‐art reanalyses. This shift toward heavier, more variable rainfall increases both the magnitude and altitude of diabatic heating associated with convective precipitation, intensifying lower tropospheric convergence and increasing the influence of convection on the upper‐level circulation. Increased diabatic production of potential vorticity in the upper troposphere intensifies the distortion effect exerted by convection on transient Rossby waves that pass through the SPCZ. Weaker distortion effects in simulations using the ZM scheme allow waves to propagate continuously through the region rather than dissipating locally, further reducing updrafts and weakening convection in the SPCZ. Our results outline a dynamical framework for evaluating model representations of tropical–extratropical interactions within the SPCZ and clarify why convective parameterizations that produce “top‐heavy” profiles of deep convective heating better represent the SPCZ and its variability. Plain Language Summary The South Pacific convergence zone (SPCZ), a band of strong rainfall that stretches diagonally across the South Pacific from northwest to southeast, is difficult for climate models to simulate well. Here, we suggest that much of this difficulty stems from underestimating both how much heavy rainfall is produced in the SPCZ and how high above the surface this rainfall forms. The SPCZ has previously been described as a “graveyard” for weather systems. Our hypothesis casts the SPCZ more as a toll collector and suggests that the vertical location of the collection point is key. Simulated weather systems that produce heavier rainfall as they move through the SPCZ region release energy higher in the atmosphere, providing the SPCZ with the means to maintain itself. A model that releases this energy lower in the atmosphere by producing too much light rain allows many weather systems to bypass the toll, weakening the simulated SPCZ and drawing it equatorward in search of the energy it needs. Key Points Biases in simulated precipitation rate affect diabatic heating and the upper‐level response to transient Rossby waves An improved deep convection parameterization reduces biases in the South Pacific Convergence Zone (SPCZ), especially for heavy rainfall More realistic upper‐level heating strengthens feedbacks between waves and convection, blocking propagation of wave energy locally