Meteorological Reanalysis Datasets Research Articles

High-resolution urban temperature simulation method considering various spatiotemporal boundary impacts

Climate change has heightened the frequency and intensity of extreme heat events in cities, greatly impacting human health, the environment, and socio-economic activities, particularly in densely populated areas. Canopy temperature (T2m) is a key indicator of whether urban area extreme heat is occurring, with significant implications for public health, energy consumption, and pollution levels. However, the diverse urban topography, functional layout, and human activities contribute to significant variations in the distribution of T2m. While computational fluid dynamics (CFD) models offer high-resolution T2m simulations, complexities in urban spatial and temporal dynamics make accurately defining boundary conditions challenging, potentially leading to large simulation errors. This study addressed the challenge of determining precise boundary conditions for urban CFD simulations by employing the Weather Research and Forecasting model to integrate meteorological reanalysis data. Different meteorological reanalysis datasets used to simulate T2m were compared, including Final Operational Global Analysis, Global Forecast System, and European Centre for Medium-Range Weather Forecasts Reanalysis v5. When combined with the reanalysis data, the minimum mean relative error of simulated T2m was 4%, which is a threefold improvement in accuracy compared to traditional meteorological boundary conditions. This study provides technical support for refined zoning of urban extreme heat risk and urban management in the context of climate change.

Spatiotemporal Modeling of Carbon Fluxes over Complex Underlying Surfaces along the North Shore of Hangzhou Bay

Urban areas contribute to over 80% of carbon dioxide emissions, and considerable efforts are being undertaken to characterize spatiotemporal variations of CO2 (carbon dioxide) at a city, regional, and national level, aiming at providing pipelines for carbon mission reduction. The complex underlying surface composition of urban areas makes process-based and physiology-based models inadequate for simulating carbon flux in this context. In this study, long short-term memory (LSTM), support vector machine (SVM), random forest (RF), and artificial neural network (ANN) were employed to develop and investigate their viability in estimating carbon flux at the ecosystem level. All the data used in our study were derived from the long-term chronosequence observations collected from the flux towers within urban complex underlying surface, along with meteorological reanalysis datasets. To assess the generalization ability of these models, the following statistical metrics were utilized: coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Our analysis revealed that the RF model performed the best in simulating carbon flux over long time series, with the highest R2 values reaching up to 0.852, and exhibiting the smallest RMSE and MAE values at 0.293 μmol·m−2·s−1 and 0.157 μmol·m−2·s−1. As a result, the RF model was chosen for simulating carbon flux at spatial scale and assessing the impact of urban impervious surfaces in the simulation. The results showed that the RF model performs well in simulating carbon flux at the spatial scale. The input of impervious surface area index can improve the performance of the RF model in simulating carbon flux, with R2 values of 84.46% (with the impervious surface area index in) and 83.74% (without the impervious surface area index in). Furthermore, the carbon flux in Fengxian District, Shanghai, exhibited significant spatial heterogeneity: the CO2 flux in the western part of Fengxian District was less than in the eastern part, and the CO2 flux gradually increased from the west to the east. In addition, we creatively introduced the diurnal impervious surface area index based on the Kljun model, and clarified the influence of impervious surface on the spatiotemporal simulation of CO2 flux over the complex urban underlying surface. Based on these findings, we conclude that the RF models can be effectively applied for estimating carbon flux on the complex underlying urban surface. The results of our study reduce the uncertainty in modeling carbon cycling in terrestrial ecosystems, and make the variety of models for the carbon cycling of terrestrial ecosystems more diverse.

Reconstruction of environmental site conditions by the integration of SCADA and reanalysis data

For the operational optimization of wind farms, AEP estimation and other tasks, high quality data of environmental conditions at the site are necessary. However, such data is often not available or has insufficient quality. This work tries to fill this gap, by integrating two data sources: the (usually available) operational data from the SCADA (Supervisory Control and Data Acquisition) system, and reanalysis data. SCADA data streams contain measurements from each wind turbine in the farm, but they are affected by various sources of uncertainty (including local flow effects, miscalibration, etc.), and might contain gaps. Meteorological reanalysis datasets can be used to fill gaps and complement SCADA data. However, modelled data can contain a wide range of biases and errors, due to limited model fidelity, coarse spatial and temporal resolution, inaccuracies in the input data feeding the model, etc. This study considers various methods to extract and merge wind speed and direction information from these diverse data sources. The analysis is based on field data measured at two experimental test sites, an offshore site equipped with 111 multi-MW turbines and a lidar buoy, and an onshore site equipped with 14 multi-MW wind turbines and a lidar. The methods are evaluated in the spectral and temporal domains by comparing the reconstructed wind characteristics with measurements from the lidars.

A satellite view of the exceptionally warm summer of 2022 over Europe

Abstract. Summer heatwaves are becoming increasingly dangerous over Europe, and their close monitoring is essential for human activities. Typically, they are monitored using the 2 m temperature from meteorological weather stations or reanalysis datasets. In this study, the 2022 extremely warm summer over Europe is analysed using satellite land surface temperature (LST), specifically the LSA SAF (Land Surface Analysis Satellite Application Facility) all-sky LST product (available from 2004 onwards). Since climate applications of LST are still poorly explored, heatwave diagnostics derived from satellite observations are compared with those derived using ERA5/ERA5-Land reanalysis data. Results highlight the exceptionality of 2022 in different metrics such as the mean LST anomaly, area under extreme heat conditions, number of hot days and heatwave magnitude index. In all metrics, 2022 ranked first when compared with the remaining years. Compared to 2018 (next in all rankings), 2022 exceeded its LST anomaly by 0.7 °C and each pixel had on average 7 more hot days. Satellite LST complements reanalysis diagnostics, as higher LST anomalies occur over areas under severe drought, indicating a higher control and amplification of the heatwave by surface processes and vegetation stress. These cross-cutting diagnostics increase the confidence across satellite data records and reanalyses, fostering their usage in climate applications.

Benchmarking multimodel terrestrial water storage seasonal cycle against Gravity Recovery and Climate Experiment (GRACE) observations over major global river basins

Abstract. The increasing reliance on global models for evaluating climate- and human-induced impacts on the hydrological cycle underscores the importance of assessing the models' reliability. Hydrological models provide valuable data on ungauged river basins or basins with limited gauge networks. The objective of this study was to evaluate the reliability of 13 global models using the Gravity Recovery and Climate Experiment (GRACE) satellite's total water storage (TWS) seasonal cycle for 29 river basins in different climate zones. Results show that the simulated seasonal total water storage change (TWSC) does not compare well with GRACE even in basins within the same climate zone. The models overestimated the seasonal peak in most boreal basins and underestimated it in tropical, arid, and temperate zones. In cold basins, the modeled phase of TWSC precedes that of GRACE by up to 2–3 months. However, it lagged behind that of GRACE by 1 month over temperate and arid to semi-arid basins. The phase agreement between GRACE and the models was good in the tropical zone. In some basins with major underlying aquifers, those models that incorporate groundwater simulations provide a better representation of the water storage dynamics. With the findings and analysis of our study, we concluded that R2 (Water Resource Reanalysis tier 2 forced with Multi-Source Weighted Ensemble Precipitation (MSWEP) dataset) models with optimized parameterizations have a better correlation with GRACE than the reverse scenario (R1 models are Water Resource Reanalysis tier 1 and tier 2 forced with the ERA-Interim (WFDEI) meteorological reanalysis dataset). This signifies an enhancement in the predictive capability of models regarding the variability of TWSC. The seasonal peak, amplitude, and phase difference analyses in this study provide new insights into the future improvement of large-scale hydrological models and TWS investigations.

Columnar optical-radiative properties and components of aerosols in the Arctic summer from long-term AERONET measurements

Aerosols as an external factor have an important role in the amplification of Arctic warming, yet the geography of this harsh region has led to a paucity of observations, which has limited our understanding of the Arctic climate. We synthesized the latest decade (2010–2021) of data on the microphysical–optical–radiative properties of aerosols and their multi-component evolution during the Arctic summer, taking into consideration the important role of wildfire burning. Our results are based on continuous observations from eight AERONET sites across the Arctic region, together with a meteorological reanalysis dataset and satellite observations of fires, and utilize a back-trajectory model to track the source of the aerosols. The summer climatological characteristics within the Arctic Circle showed that the aerosols are mainly fine-mode aerosols (fraction >0.95) with a radius of 0.15–0.20 μm, a slight extinction ability (aerosol optical depth ∼ 0.11) with strong scattering (single scattering albedo ∼0.95) and dominant forward scattering (asymmetry factor ∼ 0.68). These optical properties result in significant cooling at the Earth's surface (∼−13 W m−2) and a weak cooling effect at the top of the atmosphere (∼−5 W m−2). Further, we found that Arctic region is severely impacted by wildfire burning events in July and August, which primarily occur in central and eastern Siberia and followed in subpolar North America. The plumes from wildfire transport aerosols to the Arctic atmosphere with the westerly circulation, leading to an increase in fine-mode aerosols containing large amounts of organic carbon, with fraction as high as 97–98 %. Absorptive carbonaceous aerosols also increase synergistically, which could convert the instantaneous direct aerosol radiative effect into a heating effect on the Earth–atmosphere system. This study provides insights into the complex sources of aerosol loading in the Arctic atmosphere in summer and emphasizes the important impacts of the increasingly frequent occurrence of wildfire burning events in recent years.

Distinct responses of PM2.5 and O3 extremes to persistence of weather conditions in eastern China

Climate change may aggravate air pollution through altering emissions, ventilation, chemical production, and deposition. Under a warming climate, persistent weather patterns are likely to increase in both number and intensity, yet their implications on future air quality have been less explored. Here we use ground-level observations of air pollutants and meteorological reanalysis dataset to explore how persistence of weather conditions would affect extremes of wintertime PM2.5 and summertime O3 in China. Associated changes in the shapes of their distributions are discussed also with contrasting weather conditions. As air stagnation days persist, median summertime O3 significantly increases, with 0.07, 0.12 and 0.19 standard deviation per day (about 1.97, 3.12 and 4.63 ppb per day) for the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) regions, respectively. As high temperature days continue, significant increases in median (about 0.31 ppb per day) and 90th percentile (about 0.78 ppb per day) of summertime O3 is found only in the YRD region, although the highest probability of O3 extreme (90th percentile) is identified on the last day of four-day high temperature events for both the BTH and YRD regions. In contrast, persistent stagnation causes a significant increase in the 90th percentile of PM2.5 of about 17.87 and 10.88 μg/m3 per day for BTH and YRD regions, rather than the median. Stagnation events lasting for five, four and five days are the best indicators for PM2.5 extreme (90th percentile) in the BTH, YRD and PRD regions, respectively. Analysis on contrasting monthly weather conditions suggests that summertime O3 extreme is amplified by higher mean daily maximum air temperature (T) in the BTH region and co-occurrences of high T and stagnation in the YRD region, while wintertime PM2.5 extreme is significantly amplified by stronger stagnation. These results suggest that megacities with relatively high emissions are more vulnerable to a hotter and more stable future.

Hydrological Modeling in the Upper Lancang-Mekong River Basin Using Global and Regional Gridded Meteorological Re-Analyses

Multisource meteorological re-analyses provide the most reliable forcing data for driving hydrological models to simulate streamflow. We aimed to assess different hydrological responses through hydrological modeling in the upper Lancang-Mekong River Basin (LMRB) using two gridded meteorological datasets, Climate Forecast System Re-analysis (CFSR) and the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS). We selected the Pearson’s correlation coefficient (R), percent bias (PBIAS), and root mean square error (RMSE) indices to compare the six meteorological variables of the two datasets. The spatial distributions of the statistical indicators in CFSR and CMADS, namely, the R, PBIAS, and RMSE values, were different. Furthermore, the soil and water assessment tool plus (SWAT+) model was used to perform hydrological modeling based on CFSR and CMADS meteorological re-analyses in the upper LMRB. The different meteorological datasets resulted in significant differences in hydrological responses, reflected by variations in the sensitive parameters and their optimal values. The differences in the calibrated optimal values for the sensitive parameters led to differences in the simulated water balance components between the CFSR- and CMADS-based SWAT+ models. These findings could help improve the understanding of the strengths and weaknesses of different meteorological re-analysis datasets and their roles in hydrological modeling.

Evaluation of four meteorological reanalysis datasets for satellite-based PM2.5 retrieval over China

Meteorological reanalysis data is widely used for satellite-based retrieval of fine particulate matter (PM2.5); however, selecting appropriate data for specific regional applications remains a challenge. In China, numerous studies have used different meteorological reanalysis datasets for PM2.5 modeling, but few studies have provided their standards for data selection. In this study, we first evaluated the performance of four reanalysis datasets in China: the fifth-generation ECMWF Atmospheric Reanalysis (ERA5), the National Aeronautics and Space Administration Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA2), the GEOS-FP Atmospheric Data Assimilation System, and the Final Operational Model Global Tropospheric Analyses (FNL). The validation results show that ERA5 was the most accurate in terms of temperature, relative humidity, wind speed, and boundary layer height, whereas FNL had the highest uncertainty. The spatial accuracy of all four reanalysis datasets in the eastern region of China was notably higher than that in the western region (Qinghai-Tibet Plateau), owing to complex terrain and few ground-based observations for assimilation. We found that the use of ERA5 exhibited the best agreement with in situ measurements for PM2.5 retrieval, followed by MERRA2 and GEOS-FP, while FNL was the most inaccurate. Compared with FNL, ERA5 exhibited improved accuracy in retrieving PM2.5 during summer seasons by 12%, and reduced underestimation on heavy pollution days. Our results demonstrated that the ERA5 meteorological reanalysis is preferable for better PM2.5 retrieval in China. This study provides an actional guideline for selecting meteorological reanalysis data in subsequent satellite-based PM2.5 retrieval studies in China.

Co-occurring wintertime flooding and extreme wind over Europe, from daily to seasonal timescales

The risk posed by heavy rain and strong wind is now suspected to be exacerbated by the way they co-occur, yet this remains insufficiently understood to effectively plan and mitigate. This study systematically investigates the correlations between wintertime (Oct–Mar) extremes relating to wind and flooding at all timescales from daily to seasonal. Meteorological reanalysis and river flow datasets are used to explore the historical period, and climate projections at 12 km resolution are analysed to understand the possible effects of future climate change (2061–2080, RCP 8.5). A new flood severity index (FSI) is also developed to complement the existing storm severity index (SSI). Initially, Great Britain (GB) is taken as a comparatively simple yet informative study area, then analysis is extended to the full European domain.Aggregated across GB, wind gusts and precipitation correlate strongly (rs∼0.6–0.8) at timescales from daily to seasonal, but peak around 10 days. A later peak is seen when considering correlations between wind gusts and river flows (40–60 days). This time is likely needed for catchments’ soils to saturate. A conceptual multi-temporal, multi-process model of GB wintertime flood-wind co-occurrence is proposed as a basis for future investigation. When historical analysis is extended across Europe we find the timescale of maximum correlation varies strongly between nations, likely as a result of different meteorological drivers.Impact focused correlation (FSI–SSI) is lower (rs∼0.2) but increases notably with climate change at timescales of ∼40 days (rs∼0.4). Tentatively, very severe episodes (i.e., both >99th percentile) appear heavily influenced by climate change, increasing roughly threefold by 2061–2080 (p < 0.05). The return period of such an event is 16 years historically (compared to 56 years if the two hazards were independent), reduces to 5 years in future. Such metrics provide actionable information for insurers and other stakeholders.

The Interannual Variability of Sea Ice Area, Thickness, and Volume in the Southern Sea of Okhotsk and Its Likely Factors

AbstractThe lowest latitude sea ice in the world (excluding coastal freezing) is in the southern Sea of Okhotsk (south of 46°N), where it has significant impacts on freshwater input and primary production. This region is subject to climate change, and accordingly the monitoring of sea ice conditions is important. However, the interannual variability of the region's sea ice is poorly understood due to its logistical challenges. Sea ice observations have been conducted in this region every winter for the period 1996–2020. The interannual variability of the ice conditions and the likely factors responsible for it were investigated using visual observations following the international ASPeCt protocol, combined with satellite SSM/I‐SSMIS ice concentration data (1988–2020). AMSR‐derived ice drift data sets and ERA5 meteorological reanalysis data sets were also analyzed to examine the effects of dynamic and thermodynamic processes. Our analysis revealed that (a) sea ice area in this region varies differently from that in the central and northern Sea of Okhotsk, where decreasing trends are reported, (b) sea ice volume has remarkable interannual variation and the peaks appeared much to more affected by dynamically deformed ice than freezing conditions, and (c) prominently deformed ice can be explained by taking shear components into account based on sea ice rheology. These results suggest the importance of including the proper sea ice rheology in numerical sea ice models to reproduce the realistic sea ice volume and deformation processes, for all seasonal ice zones.

The Nord Stream pipeline gas leaks released approximately 220,000 tonnes of methane into the atmosphere

Sudden mega natural gas leaks of two Nord Stream pipelines in the Baltic Sea (Denmark) occurred from late September to early October 2022, releasing large amounts of methane into the atmosphere. We inferred the methane emissions of this event based on surface in situ observations using two inversion methods and two meteorological reanalysis datasets, supplemented with satellite-based observations. We conclude that approximately 220 ± 30 Gg of methane was released from September 26 to October 1, 2022.

Effects of reanalysis forcing fields on ozone trends and age of air from a chemical transport model

Abstract. We use TOMCAT, a 3-dimensional (3D) offline chemical transport model (CTM) forced by two different meteorological reanalysis data sets (ERA-Interim and ERA5) from the European Centre for Medium-Range weather Forecasts (ECMWF) to analyse seasonal behaviour and long-term trends in stratospheric ozone and mean age of air. The model-simulated ozone variations are evaluated against two observation-based data sets. For total column ozone (TCO) comparisons, we use the Copernicus Climate Change Service (C3S) data (1979–2019), while for ozone profiles we use the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) data set (1984–2019). We find that the CTM simulations forced by ERA-Interim (A_ERAI) and ERA5 (B_ERA5) can both successfully reproduce the spatial and temporal variations in stratospheric ozone. Also, modelled TCO anomalies from B_ERA5 show better agreement with C3S than A_ERAI, especially in Northern Hemisphere (NH) mid latitudes, except that it gives somewhat larger positive biases (&gt; 15 DU, Dobson units) during winter–spring seasons. Ozone profile comparisons against SWOOSH data show larger differences between the two simulations. In the lower stratosphere, ozone differences can be directly attributed to the representation of dynamical processes, whereas in the upper stratosphere they can be directly linked to the differences in temperatures between ERAI and ERA5 data sets. Although TCO anomalies from B_ERA5 show relatively better agreement with C3S compared to A_ERAI, a comparison with SWOOSH data does not confirm that B_ERA5 performs better at simulating the variations in the stratospheric ozone profiles. We employ a multivariate regression model to quantify the TCO and ozone profile trends before and after peak stratospheric halogen loading in 1997. Our results show that, compared to C3S, TCO recovery trends (since 1998) in simulation B_ERA5 are significantly overestimated in the Southern Hemisphere (SH) mid latitudes, while for A_ERAI in the NH mid latitudes, simulated ozone trends remain negative. Similarly, in the lower stratosphere, B_ERA5 shows positive ozone recovery trends for both NH and SH mid latitudes. In contrast, both SWOOSH and A_ERAI show opposite (negative) trends in the NH mid latitudes. Furthermore, we analyse age of air (AoA) trends to diagnose transport differences between the two reanalysis data sets. Simulation B_ERA5 shows a positive AoA trend after 1998 and somewhat older age in the NH lower stratosphere compared to A_ERAI, indicating that a slower Brewer–Dobson circulation does not translate into reduced wintertime ozone buildup in the NH extratropical lower stratosphere. Overall, our results show that models forced by the most recent ERA5 reanalyses may not yet be capable of reproducing observed changes in stratospheric ozone, particularly in the lower stratosphere.

Using the ERA5 and ERA5-Land reanalysis datasets for river water temperature modelling in a data-scarce region

It has become apparent in recent decades that river water temperature can have immediate and lasting impacts on aquatic organisms and their lotic habitat. In rivers that are dammed, there is an opportunity and a responsibility to regulate flows in order to control these temperatures to ensure the survival of the fish and other aquatic life. This paper uses a physically based hydraulic model (HEC-RAS) to run a water temperature component, allowing the thermal model to simulate water temperatures at the same hourly time step as the hydraulic model in a data-sparse region using two meteorological reanalysis datasets (ERA5 and ERA5-Land) as inputs allowing for a full representation of the diurnal cycle. This was achieved by making use of the HEC-RAS controller to automate the calibration and subsequent simulation processes. Results show that these products are able to provide high-quality thermal simulations on a 200 km river system in British Columbia, Canada, obtaining mean absolute errors in validation of 0.66 °C and a root mean square error of 0.84 °C. Some of the boundary conditions seemed to have little effect on downstream water temperatures. This is due to the measured point of interest being far enough downstream of the dam that a thermal equilibrium is reached well before. Simulations using shorter river reaches confirm that long lakes in the study region contribute to the thermal equilibrium being attained. There also seems to be a limit to the advantage conveyed by increased spatial density of the data, as results indicate a form of skill plateau after a certain input data density is attained.

Analyzing the mechanisms behind temporal correlation between power sources using frequency separated time scales: A Swedish case study on PV and wind

The temporal correlation between different power generation sources is important for quantifying the reduction in variability when constructing co-located hybrid power parks (HPPs) that combine multiple power sources. This study investigates the physical mechanisms behind correlation on time scales relevant for the power system using frequency separated time scales. The methodology is universally applicable to any data set consisting of at least two power sources and could be adjusted accordingly. The methodology is demonstrated and validated in a case study across Sweden for wind and PV power generation, using the meteorological reanalysis dataset CosmoREA-6. All studied time-scales (seasonal, mid-term, synoptic and diurnal) showed anti-correlated characteristics, although the magnitude of temporal correlation is highly dependent on the time-scale considered. The highest potential for useful anti-correlation is found on the seasonal cycle, followed by the diurnal cycle where existing wind turbine sites, on average, have stronger anti-correlation than the average site. The validation showed good correspondence with measurements for all time-scales. However, an underestimations of the results were found for the diurnal and seasonal cycle while this was shown to have a minor effect when analyzing the correlation on different time scales. The methodology of the case study should be generally valid for all similar climates.

Assessment of global reanalysis precipitation for hydrological modelling in data-scarce regions: A case study of Kenya

Study region19 flood prone catchments in Kenya, Eastern Africa Study focusFlooding is a major natural hazard especially in developing countries, and the need for timely, reliable, and actionable hydrological forecasts is paramount. Hydrological modelling is essential to produce forecasts but is a challenging task, especially in poorly gauged catchments, because of the inadequate temporal and spatial coverage of hydro-meteorological observations. Open access global meteorological reanalysis datasets can fill in this gap, however they have significant errors. This study assesses the performance of four reanalysis datasets (ERA5, ERA-Interim, CFSR and JRA55) over Kenya for the period 1981–2016 on daily, monthly, seasonal, and annual timescales. We firstly evaluate the reanalysis datasets by comparing them against observations from the Climate Hazards group Infrared Precipitation with Station. Secondly, we evaluate the ability of these reanalysis datasets to simulate streamflow using GR4J model considering both model performance and parameters sensitivity and identifiability. New hydrological insights for the regionWhile ERA5 is the best performing dataset overall, performance varies by season, and catchment and therefore there are marked differences in the suitability of reanalysis products for forcing hydrological models. Overall, wetland catchments in the western regions and highlands of Kenya obtained relatively better scores compared to those in the semi-arid regions, this can inform future applications of reanalysis products for setting up hydrological models that can be used for flood forecasting, early warning, and early action in data scarce regions, such as Kenya.

Multiscale Causes of Persistent Heavy Rainfall in the Meiyu Period over the Middle and Lower Reaches of the Yangtze River

Based on observation data supplied by the Chinese Meteorological Administration (CMA) and reanalysis datasets provided by the ECMWF, the multiscale causes of persistent heavy rainfall events (PHREs) that occurred from 1979 to 2018 during Meiyu periods over the middle and lower reaches of the Yangtze River (MLYR) are investigated. During Meiyu periods, precipitation shows obvious interannual variabilities. In PHRE years, the contribution rate of persistent heavy rainfall to the total precipitation is approximately 57%. Precipitation also shows significant synoptic-scale (less than 10 days) characteristics. Through the quantitative diagnosis of interactions among background-scale (greater than 30 days), quasi-biweekly-scale (10–30-days), and synoptic-scale variables, the possible causes of PHREs are explored. The results reveal that the difference in precipitation intensity between PHRE years and non-PHRE years is determined by the background water vapor, background wind and synoptic-scale wind conditions. In PHRE years, the prevailing background southwesterly winds from lower latitudes provide more background water vapor, and more mean kinetic energy is converted to perturbation energy. Moreover, the active synoptic-scale oscillations from higher latitudes and the convergence of Rossby wave disturbance energy over the MLYR could also cause the occurrence and maintenance of PHREs during Meiyu periods. The multiscale causes and corresponding circulation patterns in 2020 PHREs are similar to PHREs years.

Evaluating the complementarity of solar, wind and hydropower to mitigate the impact of El Niño Southern Oscillation in Latin America

Latin America has the largest share of renewable energy for power generation in the world, but has historically been dependent on hydropower, and is vulnerable to long-term phenomena like the El Niño-Southern Oscillation (ENSO). The region is currently experiencing a steady increase in gas-based power generation along with a rapid growth in non-hydro renewables, mainly aimed at improving reliability. But exploiting complementarities between hydropower and other renewables could offer additional benefits. This paper investigates to what extent improved deployment of wind turbines and solar photovoltaic cells (PV), aimed at complementing existing hydropower, could mitigate the impacts of ENSO in Latin America. We use a meteorological reanalysis dataset to model the variations in wind-, solar- and hydropower throughout the entire 20th century and their association with different ENSO phases in the same period. A statistical algorithm is then used to identify locations at national and regional levels offering the maximal level of complementarity. The results show that adding 136 GW of wind- and solar-power with high-complementarity has the potential to cost-effectively compensate the fluctuations of hydropower and reduce the variability of renewable power not only during drought ENSO phases but also outside ENSO events. Benefits include: (i) an increase of up to 5-fold the minimum threshold of renewable power available every month, (ii) a reduction of up to 65% in the variability of renewable power generation, and (iii) reaching a levelized cost of electricity (LCOE) of 0.09–0.23 $/kWh, which is comparable to that of new gas plants.

Potential Associations between Low-Level Jets and Intraseasonal and Semi-Diurnal Variations in Coastal Chlorophyll—A over the Beibuwan Gulf, South China Sea

Low-level jet (LLJ) significantly affects the synoptic-scale hydrometeorological conditions in the South China Sea, although the impact of LLJs on the marine ecological environment is still unclear. We used multi-satellite observation data and meteorological reanalysis datasets to study the potential impact of LLJs on the marine biophysical environment over the Beibuwan Gulf (BBG) in summer during 2015–2019. In terms of the summer average, the sea surface wind vectors on LLJ days became stronger in the southwesterly direction relative to those on non-LLJ days, resulting in enhanced Ekman pumping (the maximum upwelling exceeds 10 × 10−6 m s−1) in most areas of the BBG, accompanied by stronger photosynthetically active radiation (increased by about 20 μmol m−2 s−1) and less precipitation (decreased by about 3 mm day−1). These LLJ-induced hydrometeorological changes led to an increase of about 0.3 °C in the nearshore sea surface temperature and an increase of 0.1–0.5 mg m−3 (decrease of 0.1–0.3 mg m−3) in the chlorophyll-a (chl-a) concentrations in nearshore (offshore) regions. Intraseasonal and diurnal changes in the incidence and intensity of LLJs potentially resulted in changes in the biophysical ocean environment in nearshore regions on intraseasonal and semi-diurnal timescales. The semi-diurnal peak and amplitude of chl-a concentrations on LLJ days increased with respect to those on non-LLJ days. Relative to the southern BBG, LLJ events exhibit greater impacts on the northern BBG, causing increases of the semi-diurnal peak and amplitude with 1.5 mg m−3 and 0.7 mg m−3, respectively. This work provides scientific evidence for understanding the potential mechanism of synoptic-scale changes in the marine ecological environment in marginal seas with frequent LLJ days.

Influence of stratospheric sudden warming on the tropical intraseasonal convection

Madden–Julian oscillation (MJO), the dominant mode of intraseasonal variability in the tropical troposphere, has recently been shown to have a great impact on Northern Hemisphere (NH) extratropical stratosphere. But the influence of the variability in the extratropical stratosphere on MJO is seldom reported. In this study, the influence of major, mid–winter NH stratospheric sudden warmings (SSWs) on the MJO is investigated using meteorological reanalysis datasets. Our analysis reveals that SSWs also exert considerable influence on tropical intraseasonal convection. The occurrences of MJO phases 6 and 7 significantly increase during around 20 d after the onset of SSWs, corresponding to enhanced convective activity over the equatorial Central and Western Pacific. Then in the following days, the coherent eastward propagation of tropical intraseasonal convection resembles the periodic variation in a typical MJO. These results suggest that the extratropical stratosphere affects the organized tropical intraseasonal convection, and variability of the tropical intraseasonal convection related to MJO can be better grasped by taking extratropical stratospheric variability into account. Considering the complex interaction between MJO and extratropical stratosphere, further work on comprehensive understanding of the relationship between SSWs and MJO is required in future studies.