Atmospheric Reanalysis Research Articles

Revisiting the Inconsistent Influence of El Niño–Southern Oscillation on the Equatorial Atlantic

Abstract The impact of El Niño–Southern Oscillation (ENSO) on the equatorial Atlantic zonal mode (AZM) is investigated using two atmospheric reanalysis products and 44 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6). While the impact of ENSO on equatorial Atlantic SST is inconsistent, as previously reported, there is a very robust influence of decaying ENSO events in boreal spring on the contemporaneous surface zonal winds over the equatorial Atlantic, with El Niño events associated with anomalous easterlies that cool the equatorial Atlantic. This dynamic impact is counteracted by a thermodynamic impact, in which El Niño events cause changes in surface heat fluxes that lead to warming over the tropical Atlantic. The thermodynamic influence is active throughout the life cycle of the ENSO event, but the dynamic influence is highly seasonal with a pronounced peak in boreal spring. Thus, a quickly decaying El Niño event will lead to warming, while a slowly decaying event will lead to cooling in the equatorial Atlantic. The ENSO–AZM relation is further complicated by partially independent Atlantic variability, in particular that associated with the South Atlantic subtropical dipole (SASD). Prediction experiments with a simple linear regression model support the role of the SASD as an AZM precursor. Many CMIP6 models show skillful predictions of the June–August AZM based on SST indices in the preceding December–February, with correlations above 0.5. When predictions are restricted to ENSO years, even more models exceed this threshold, but skill in the reanalyses remains relatively low, possibly due to insufficient training data. Significance Statement El Niño is the strongest climate signal on interannual time scales. Its influence on the equatorial Atlantic, however, is surprisingly inconsistent. Here, we show, using an ensemble of climate models and observation-based data, that El Niño has a very robust influence on equatorial Atlantic surface winds, but only in boreal spring. This leads to a large difference in how El Niño events influence the equatorial Atlantic, based on how quickly they decay in boreal spring. We also find that variability in the South Atlantic, which is partly independent of El Niño, has a strong influence on the equatorial Atlantic, supporting the result of previous studies. These findings suggest that the predictability of equatorial Atlantic temperature anomalies may be higher than previously thought.

Quantification of Soil–Water Erosion Using the RUSLE Method in the Mékrou Watershed (Middle Niger River)

Despite nearly a century of research on water-related issues, water erosion remains one of the greatest threats to soil health and soil ecosystem services around the world. Yet, to date, data on water erosion needed to develop mitigation strategies are scarce, especially in the Sahelian regions. The current study therefore sets out to estimate annual soil losses caused by water erosion and to analyze trends over the period of 1981–2020 in the Mékrou watershed, located in the Middle Niger river sub-basin in West Africa. The Revised Universal Soil Loss Equation, remote sensing, and the Geographic Information System (GIS) were deployed in this study. Several types of data were used, including rainfall data, sourced from meteorological stations and reanalysis datasets, which capture the temporal variability of erosive forces. Soil properties, including texture and organic matter content, were derived from FAO global soil databases to assess soil erodibility. High-resolution digital elevation models (30 m) provided detailed topographic information, crucial for calculating slope length and steepness factors. Land use and land cover data were extracted from satellite imagery, enabling the analysis of vegetation cover and anthropogenic impacts over four decades. By integrating and treating these data, this study reveals that the estimated average annual amount of water erosion in the Mékrou watershed is 6.49 t/ha/yr over 1981–2020. The dynamics of the ten-year average are highly variable, with a minimum of 3.45 t/ha/yr between 1981 and 1990, and a maximum of 8.50 t/ha/yr between 1991 and 2000. Even though these average soil losses in the Mékrou basin are below the tolerable threshold of 10 t/ha/yr, mitigation actions are needed for prevention. In addition, the spatial dynamics of water erosion are noticeably heterogeneous. The study reveals that 72.7% of the surface area of the Mékrou watershed is subject to slight water erosion below the threshold, compared with 27.3%, particularly in the mountainous south-western part, which is subject to intense erosion above the threshold. This research is the first study of soil erosion quantification with the RUSLE method and GIS in the Mékrou watershed, and fills a critical knowledge gap of the water erosion in this watershed, providing insights into erosion dynamics and supporting future sustainable land management strategies in vulnerable Sahelian landscapes.

Spatio-temporal Variations of Heat Extremes across the Yangtze River Delta during 2001-2023 Based on Remotely Sensed Seamless Air Temperature.

Heat extremes become increasingly frequent and severe, posing adverse risks to public health and environment. Previous research on extreme heat mostly used meteorological observations or reanalysis data, which cannot well capture detailed spatial patterns. This study developed a seamless air temperature (Ta) dataset from remote sensing data to characterize the spatio-temporal variations of heat extremes in the Yangtze River Delta (YRD) from 2001 to 2023. First, the daily maximum Ta of cloud-free pixels was estimated through machine learning algorithms from MODIS land surface temperature (LST) and other remote sensing data. Then, gaps in the estimated Ta caused by cloud cover were filled using the Temporal Fourier Analysis (TFA) method, generating a seamless daily maximum Ta dataset. The remotely sensed Ta achieved an overall MAE of 1.11°C. Based on the remotely sensed Ta, six heat indices were calculated to characterize heat extremes, including heat days (HTD), effective accumulated high temperature (EAHT), heatwave frequency (HWF), cumulative heatwave days (HWD), maximum heatwave duration (HWMD) and average heatwave duration (HWAD). Heat extremes occurred frequently in the YRD, with obvious spatial variability. Southern basins experienced intense heat with high frequency and duration, while southern mountains and northern areas experienced weaker heat extremes. Urban areas have substantially more intense heat events than suburbs, attributed to urban heat island effect. 2022 recorded the most severe heat, with notable events also in 2013 and 2003. This study provides valuable insights into heat events in the YRD and serves as a reference for remote sensing research on heat events.

The JAGUAR-DAS whole neutral atmosphere reanalysis: JAWARA

Using the Japanese Atmospheric General circulation model for the Upper Atmosphere Research-Data Assimilation System (JAGUAR-DAS), a whole neutral atmosphere reanalysis dataset (JAWARA) over about 19 years from September 2004 to December 2023 is produced. JAWARA is the first long-period reanalysis covering the height region from the surface to the lower thermosphere (~ 110 km). This wide height coverage is a notable advantage over other operational reanalysis datasets, which cover up to the middle mesosphere. Key dynamical characteristics are compared between JAWARA and two satellite observations and three other operational reanalysis datasets in their covered height regions. The seasonal variations of zonal mean temperature and zonal wind are similar between JAWARA and the datasets used for comparison. The climatologies of zonal mean temperature, zonal wind, residual-mean circulation, and E-P flux in the meridional cross section are also broadly consistent with other reanalysis datasets. The analyzed residual-mean vertical flow in the northern high latitudes in the middle atmosphere exhibits the well-known patterns of upwelling in summer and downwelling in winter. JAWARA also shows a prominent feature of strong downward propagating anomalies from the lower thermosphere to the upper stratosphere after sudden stratospheric warmings. This analysis takes full advantage of the JAWARA data, which cannot be made using satellite observations and other reanalysis datasets. This reanalysis product is expected to contribute broadly to future research on the characteristics of observed mesospheric phenomena, thermosphere–ionosphere coupling, space weather, and improvement of middle atmospheric meteorological systems, including their interannual and decadal scale variability.

E_GSMaP precipitation dataset reforecasted by RF-WMRA: Description and validation.

Accurate precipitation data is an important state variable in various application fields such as geohazard early warning, flood, and drought hazard monitoring and evaluation. This study proposed a precipitation reforecast inversion model based on Random Forest and Wavelet Multi-Resolution Analysis (RF-WMRA) method. In the basin along the Sichuan-Tibet Railway, we constructed the reforecast inversion model by using the fifth generation of atmospheric reanalysis of the European Centre for Medium-Range Weather Forecasts (ERA5) and Global Satellite Mapping of Precipitation (GSMaP) precipitation data and considering several geo-environmental variables such as elevation, humidity, temperature, and wind speed. In turn, we could obtain a set of E_GSMaP precipitation data with high spatial resolution (0.1°) from 1961 to 2020 in the study area. In addition, we also compared and analyzed the accuracy of the E_GSMaP data with both the ERA5 and GSMaP data from the perspective of extreme precipitation event monitoring. The results showed that: 1) The Correlation Coefficient (CC) of the RF reforecast model was 0.89, Root Mean Squared Error (RMSE) was 1.65mm, Relative Bias (BIAS) was 0.01%, Mean Absolute Error (MAE) was 0.42mm, and Kling-Gupta efficiency (KGE) was 0.80. CC and KGE increased to various degrees before and after WMRA model correction, RMSE, BIAS, and MAE decreased to various degrees. It indicated that this study's RF-WMRA reforecast inversion model performed better. 2) If extreme precipitation events in the study area were analyzed, we recommend the E_GSMaP data given the performance of three datasets, E_GSMaP, ERA5, and GSMaP, on seasonal and monthly time scales as well as in total extreme precipitation. The results of this study can provide basic data for regional geological disasters and hydrometeorological research and help to respond to the risk of geological disasters and climate change.

Validation of the Ensemble Generalized Analog Regression Downscaling (En-GARD) Model to Downscale Near-Surface Wind Speed for the Northeast United States

Abstract Wind, often overlooked in climate change impact assessments, is now a crucial atmospheric variable due to our imminent reliance on renewable energy sources like wind energy. Therefore, establishing a wind speed database in higher temporal and spatial resolution is of great importance to assess wind power in historical periods and future projections. This study focuses on enhancing wind data representation and reliability of statistically downscaled model outputs by refining grid spacing from coarser to a desirable finer scale. Employing the Ensemble Generalized Analog Regression Downscaling (En-GARD) technique, we downscaled near-surface wind speed across a northeast (NE) U.S. domain that combines onshore and offshore wind farm areas. We tested multiple atmospheric variable combinations and identified near-surface wind speed (wind speed at 10 m), longitudinal and latitudinal wind at 10 m, and atmospheric temperature at 2 m, as the set that effectively guides the selection of analog days for the analog regression downscaling approach. Validation involved using ERA5 atmospheric reanalysis products (31 km) and high-resolution (4 km) Weather Research and Forecasting (WRF) Model simulations. Downscaling was conducted using a leave-one-year-out approach over 12 years (2001–12). Statistical analysis of the downscaled output over this period demonstrated a significant correlation and low error metrics (less than 20% for normalized RMSE and 16% for normalized MAE), indicating the reliability of the method and paving the way for its future application to downscale climate projection outputs. Significance Statement Wind speed has become a significant atmospheric variable due to our society’s imminent reliance on renewable energy resources like wind power. Often overlooked in climate change assessments, wind speed at a local scale over wind farm locations plays a crucial role in assessing and planning wind power resources in a future climate. We are providing the successful validation of a statistical downscaling methodology for near-surface wind speed at 4-km grid spacing using reanalysis data. The validity of the methodology gives confidence to downscale climate projection models and assess changes in wind speed for future climate scenarios in a follow-up study.

On the Quantification of Thermodynamic Phases of Raining Clouds: Insights From Multi‐Year CloudSat and Ground‐Based Radar Observations Over Longmen, Southern China

AbstractThe thermodynamic phase of clouds is fundamental to rain initiation and development. Although it is well established that majority of rainfall from mid‐ to‐high latitudes originates from ice, there is a lack of consensus on the major thermodynamic phase of raining clouds over low latitudes. Previous CloudSat‐based studies have yet reached consistent results on the fraction of ice‐phase raining clouds over Southern China. In this work, we found that CloudSat‐based approaches are subject to metrics used for flagging rainfall events, which may lead to different estimates. Analysis of multi‐year surface radar observations suggests that has diurnal variation and increases with rain rate, while the surface rainfall originating from warm clouds does not exceed 50 . Using disdrometer observations to flag surface rainfall events, we found that about 71 and 95 of surface rainfall events and rainfall accumulation come from cold clouds, respectively. In addition, the observed rainfall accumulations over Longmen are consistent with the fifth generation atmospheric reanalysis (ERA5), whereas the frequency of warm rainfall over Longmen is significantly underestimated in ERA5.

АВТОМАТИЧЕСКАЯ ИДЕНТИФИКАЦИЯ НОВОЗЕМЕЛЬСКОЙ БОРЫ

The paper examines a type of downslope windstorm – the Novaya Zemlya bora. This is a insufficiently studied mesoscale phenomenon characterized by the presence of high wind speeds on the western slope of the archipelago, threatening the safety of port buildings and maritime navigation. The paper proposes an approach for automatic identification of bora, which will allow to make a climatic picture of this phenomenon and to identify its characteristic features. The developed approach was applied to long-term (2015–2023) high-resolution numerical simulation data obtained using the WRF atmospheric model, and to lower-resolution data – atmospheric reanalysis ERA5. Over a 9-year period, about 220 bora events were analyzed, and this was done not only for the entire archipelago (which is typical for most studies), but also for individual regions of Novaya Zemlya. It was shown that the qualitative climatic characteristics of bora do not depend on spatial resolution, which potentially allows us to apply the developed method to identify the bora on longer time periods, for example, the entire period covered by the ERA5 reanalysis data. However, on a quantitative level, high resolution showed a greater intensity and duration of the phenomenon, as expected.

The Influence of North Atlantic Dipole Sea Surface Temperature Anomalies on Autumn Drought Over Southwest China

ABSTRACTIn recent decades, there has been a notable increase in the frequency and severity of drought events in Southwest China (SWC), which have significantly impacted agriculture and the social economy. Using sea surface temperature (SST) data from the Hadley Centre, daily meteorological drought composite index grid data and ERA5 reanalysis data, we investigate the characteristics of autumn drought in SWC and discuss its possible causes using empirical orthogonal function (EOF) analysis. The results indicate a distinct ‘Northeast–Southwest’ dipole pattern of autumn drought over SWC in the past decade, which we define as the ‘Chuan‐Yu’‐type drought. A British–Okhotsk Corridor (BOC) pattern Rossby wave train, presenting over Eurasia, is identified as the key factor influencing the ‘Chuan‐Yu’‐type drought in SWC during the autumn. The BOC‐pattern Rossby wave train not only obstructs water vapour transport channels in SWC but also induces anomalous descending motions in the lower to middle troposphere over the region, leading to high temperatures and insufficient precipitation. Further investigation reveals that North Atlantic dipole SST anomalies trigger the BOC‐pattern Rossby wave train in autumn. The results of the linear baroclinic model sensitivity simulations support the above conclusion. These findings will make valuable contributions to autumn drought prevention and mitigation in SWC.

Tashanta as a promising astropoint of the Altai mountains: the first results of the astroclimate study

For the Tashanta point, 49°43ʹ4ʺN, 89°11ʹ31ʺE, Gorny Altai, data from continuous measurements of astroclimatic parameters of the surface layer, monthly average values of the total cloud cover, suspended water vapor from the ERA5 atmospheric reanalysis database and primary measurements of the altitude distribution of turbulence are presented. The results show unique astroclimatic and operational conditions, which probably make it one of the best places in the Russian Federation for submillimeter and optical range observatories.

Замерзающие осадки на Европейской территории России в 1979–2022 гг. и их воспроизведение реанализом ERA5

The quality of reproduction of freezing precipitation by the ERA5 reanalysis in European Russia is assessed. The analysis was performed using the data of the main 3 hour observations of weather phenomena and the ERA5 atmospheric reanalysis data with a step of 0.25° in space and 1 hour in time. The maps of the average number of days with freezing precipitation in the form of drizzle, rain, and their combination were drawn for the period from 1979 to 2022. Similar maps of the average number of days with freezing rain were obtained from the ERA5 data on the precipitation type. It is shown that the ERA5 successfully reproduces individual cases of high-intensity freezing precipitation formed under the stratification of the “warm nose” type. When comparing the reanalysis data on the number of simulated cases of freezing rain with observational data from 214 meteorological stations, the coefficient of determination R2 was 0.291 for freezing rain and 0.14 for freezing drizzle. The low values of the coefficients of determination are related to the fact that the formation of freezing rain in the ERA5 is possible only in the presence of a melting layer, due to which the predominant number of cases of freezing precipitation occurring in a completely cold atmosphere is not reproduced by the reanalysis. The ERA5 data on the precipitation type are most useful for analyzing the conditions of high-intensity freezing rain formed according to the “classic mechanism”.

How Much Convective Environment Subgrid Spatial Variability Is Missing Within Atmospheric Reanalysis Data Sets?

AbstractConvective cloud processes are sensitive to environmental conditions that vary on scales smaller than reanalysis data sets (sub‐reanalysis scales). Convective environment variability within areas representative of reanalysis data sets is quantified using large‐domain, high‐resolution (∆x = 100 m) simulations of convective cloud systems throughout the tropics and subtropics. Even after removing locations of resolved clouds and precipitation, convective environment parameters vary significantly on these scales. For example, for half of the simulated data, 500 hPa relative humidity varies by ∼30% within a typical reanalysis area. Surface winds, convective available potential energy, and middle‐tropospheric moisture are the most variable convective environment parameters for both continental and maritime regimes, while above‐surface temperature and winds are the least variable. While high‐resolution, sub‐reanalysis‐scale extrema are well‐correlated with the reanalysis‐area mean values, some of the most extreme convective environments can occur within regions with moderate reanalysis‐area mean values, particularly for continental regions.

Оценка объемной влажности почвы реанализа ERA5 по данным станционных наблюдений влагозапасов в регионах Российской Федерации

The study compares volumetric soil moisture based on the data from the fifth-generation global climate reanalysis (ERA5) and observations of productive soil moisture reserves in various soil layers at ten Roshydromet stations for the growing seasons from 2011 to 2023. The coordination of these series in mm of the water layer of total moisture with account of biases is carried out. It is shown that the reanalysis reproduces the main features of seasonal variations in soil moisture in the layers of 0–50 and 0–100 cm, its dynamics during the growing seasons, as well as the episodes of excessive moisture or drought conditions.

Growing aridity poses threats to global land surface

Global warming has impacted water cycle, but not exist a global study of the changes at global scale of the impacts on water available for plants. Here, cloud-optimized monthly aggregated climate reanalysis from the European Centre for Medium-Range Weather Forecasts dataset indicates that from 1960 to 2023, 27.9% of the global land surface became significantly more arid, while 20.5% became significantly less arid. This indicates a shift towards drier climates, with humid, semi-humid, and semi-arid areas decreasing by 8.51, 1.45, and 0.53 million-km², respectively, and arid and hyper-arid areas increasing by 6.34 and 4.18 million-km², respectively. This total increase of 9.99 million km² in arid areas represents 5.9% of the global land surface, excluding Greenland and Antarctica. Accelerated aridification has occurred in already dry regions, such as South-west North-America, North-Brazil, the European-Basin, North-Africa, the Middle-East, the Sahel, and central-Asia, with central-Africa as a new hotspot. The main driver is the disproportionate increase in potential evapotranspiration relative to rainfall, attributed to rising atmospheric temperatures, which also reduces the land’s carbon sink capacity, potentially exacerbating climate warming.

Contrasting two major Arctic coastal polynyas: the role of sea ice in driving diel vertical migrations of zooplankton in the Laptev and Beaufort seas

Abstract. The diel vertical migration (DVM) of zooplankton is one of the largest species migrations to occur globally and is a key driver of regional ecosystems and the marine carbon pump. Previously thought to be hampered by the extreme light regime prevailing in the Arctic Ocean, observations have revealed that DVM does occur in ice-covered Arctic waters and suggest the decline in Arctic sea ice may thereby impact DVM and its role in the Arctic ecosystem. However, coastal polynyas present a unique environment where open water or thin, nearly translucent, ice prevail when offshore winds advect the ice pack away from the coast, allowing light into the surface waters and potentially disrupting DVM. Here, four yearlong time series of acoustic backscatter collected by moored acoustic Doppler current profilers at two opposite sides of the circumpolar polynya system at the Laptev Sea shelf (2007–2008) and the Beaufort Sea shelf (2005–2006) were used to examine the annual cycle of acoustic scattering and therefore the annual cycle of DVM in these areas. The acoustic time series were used along with atmospheric and oceanic reanalysis and satellite data to interpret the results. Our observations show that DVM started to occur once the ice-free surface or under-ice layer irradiance exceeds a certain threshold (from ∼0.3 to 3.3 lx), which is about 2 to 10 times lower in the Beaufort Sea compared to the Laptev Sea. In the Laptev Sea, based on our data and methodology, DVM could not be detected during polar night. In contrast, civil twilight in the Beaufort Sea is sufficient to trigger DVM through polar night. This difference in DVM between the Laptev and Beaufort seas is not entirely assigned to the 3° difference in latitude between the mooring positions as it is also due to the different light threshold required to trigger DVM, different zooplankton communities' composition, and potentially different depths and predation pressures. We find examples in both the Laptev and Beaufort seas where the formation of polynyas and large leads caused DVM to abruptly cease or be disrupted, which we attribute to predator avoidance by the zooplankton in response to higher polar cod (Boreogadus saida) abundance near the open water. Finally, light attenuation by sea ice in the Beaufort Sea caused DVM to extend onto the polar day until the summer solstice. Overall, our results highlight the role of sea ice in disrupting synchronized DVM, the spatial variability in the relationship between sea ice and DVM, and the potential ecological impact of significant trends toward a more extensive circumpolar Arctic coastal polynya as part of changing ice conditions in the Arctic Ocean.

Zonally Asymmetric Increase in Southern Ocean Heat Content

Abstract A zonally symmetric perspective has proven to be very useful in studying the key role of the Southern Ocean for global ocean heat uptake. Despite this, reconstructions of changes in Southern Ocean heat content ΔOHC over the past few decades have revealed substantial deviations from symmetry. Here, we investigate the zonal asymmetry of ΔOHC and the processes driving it for the period 1979–2019 using an eddy-permitting ocean model forced by atmospheric reanalysis (ERA5). We find that the model successfully reproduces the observed zonal asymmetry in ΔOHC and that it is primarily due to zonal asymmetries in changing surface fluxes. Zonal asymmetries in mean ocean circulation play an important secondary role. Factorial simulations are used to attribute the asymmetry in ΔOHC to the different surface flux components (wind stress, heat, and freshwater fluxes), revealing strong regional differences. North of the Antarctic Circumpolar Current (ACC), we find roughly equal contributions from changes in wind stress and surface heat flux. Within the ACC, all three forcings play an important role, while south of it only wind stress and freshwater flux changes are important. Our findings argue for full consideration of the three-dimensional circulation to understand heat uptake and storage in the Southern Ocean, especially when considering multidecadal time scales where natural variations are as important as the long-term warming trend.

An Investigation of the SOCOLv4 Model’s Suitability for Predicting the Future Evolution of the Total Column Ozone

The anthropogenic impact on the ozone layer is expressed in anomalies in the total ozone content (TOC) on a global scale, with periodic enhancements observed in high-latitude areas. In addition, there are significant variations in TOC time trends at different latitudes and seasons. The reliability of the TOC future trends projections using climate chemistry models must be constantly monitored and improved, exploiting comparisons against available measurements. In this study, the ability of the Earth’s system model SOCOLv4.0 to predict TOC is evaluated by using more than 40 years of satellite measurements and meteorological reanalysis data. In general, the model overpredicts TOC in the Northern Hemisphere (by up to 16 DU) and significantly underpredicts it in the South Pole region (by up to 28 DU). The worst agreement was found in both polar regions, while the best was in the tropics (the mean difference constitutes 4.2 DU). The correlation between monthly means is in the range of 0.75–0.92. The SOCOLv4 model significantly overestimates air temperature above 1 hPa relative to MERRA2 and ERA5 reanalysis (by 10–20 K), particularly during polar nights, which may be one of the reasons for the inaccuracies in the simulation of polar ozone anomalies by the model. It is proposed that the SOCOLv4 model can be used for future projections of TOC under the changing scenarios of human activities.

Seasonal snow cover indicators in coastal Greenland from in situ observations, a climate model, and reanalysis

Abstract. Seasonal snow cover has important climatic and ecological implications for the ice-free regions of coastal Greenland. Here we present, for the first time, a dataset of quality-controlled snow depth measurements from nine locations in coastal Greenland with varying periods between 1997 and 2021. Using a simple modelling approach (Δsnow), we estimate snow water equivalent values solely based on the daily time series of snow depth. Snow pit measurements from two locations enable us to evaluate the Δsnow model. As there are very little in situ data available for Greenland, we then test the performance of the regional atmospheric climate model (RACMO2.3p2, 5.5 km spatial resolution) and reanalysis product (CARRA, 2.5 km spatial resolution) at the nine locations with snow observations. Using the combined information from all three data sources, we study spatio-temporal characteristics of the seasonal snow cover in coastal Greenland using the example of six ecologically relevant snow indicators (maximum snow water equivalent, melt onset, melt duration, snow cover duration, snow cover onset, and snow cover end date). In particular, we evaluate the ability of RACMO2.3p2 and CARRA to simulate these snow indicators at the nine different locations, perform a time series analysis of the indicators, and assess their spatial variability. The different locations have considerable spatial and temporal variability in snow cover characteristics, and seasonal maximum snow water equivalent (amount of liquid water stored in the snowpack) values range from less than 50 kg m−2 to greater than 600 kg m−2. The correlation coefficients between maximum snow water equivalent output from Δsnow and CARRA and RACMO are 0.73 and 0.48, respectively. Correlation coefficients are highest for maximum snow water equivalent and snow cover duration, and model and reanalysis output underestimate snow cover onset. We find little evidence of statistically significant (p<0.05) trends at varied periods between 1997 and 2021 except for the earlier onset of snowmelt in Zackenberg (−8 d per decade, p=0.02, based upon RACMO output). While we stress the need for context-specific validation, this study suggests that in most cases snow depth or snow water equivalent output from CARRA can describe spatial and temporal characteristics of seasonal snow cover, particularly changes in melt onset and snow cover end date.

Why Super Typhoon Occurrence Over the Western North Pacific Ocean Tends to be More in Autumn Than Summer

ABSTRACTBased on the tropical cyclone (TC) best‐track datasets from the China Meteorological Administration during 1949–2020 and the fifth generation ECMWF atmospheric reanalysis (ERA5) datasets, we investigate the characteristics of super typhoons (SuperTYs) over the western North Pacific (WNP) and associated mechanism in this study. The results show that SuperTYs are prone to occur in autumn over the WNP, nearly 30% of the autumn TCs develop into SuperTYs, and autumn SuperTYs account for more than half of the annual total. This is due to both favourable oceanic and atmospheric conditions. In southeastern WNP, the sea surface temperature (SST) in autumn is higher than that in summer, inducing zonal circulation anomalies and enhancing low‐level westerlies. Consequently, the monsoon trough strengthens and extends eastward, favouring enhanced autumn typhoon occurrence in the southeastern WNP. This southeastward shift facilitates TCs to remain over the warm ocean for a longer period and makes them more prone to develop into SuperTYs. Furthermore, TCs tend to take westward‐moving tracks in autumn due to stronger easterly steering flows compared with summer, resulting in more TCs passing over the South China Sea (SCS) to the east of the Philippines where the vertical wind shear (VWS) is relatively weaker and prone to develop into superTYs.

Evaluating the Two-Source Energy Balance Model Using MODIS Data for Estimating Evapotranspiration Time Series on a Regional Scale

Estimating daily continuous evapotranspiration (ET) can significantly enhance the monitoring of crop stress and drought on regional scales, as well as benefit the design of agricultural drought early warning systems. However, there is a need to verify the models’ performance in estimating the spatiotemporal continuity of long-term daily evapotranspiration (ETd) on regional scales due to uncertainties in satellite measurements. In this study, a thermal-based two-surface energy balance (TSEB) model was used concurrently with Terra/Aqua MODIS data and the ERA5 atmospheric reanalysis dataset to calculate the surface energy balance of the soil–canopy–atmosphere continuum and estimate ET at a 1 km spatial resolution from 2000 to 2022. The performance of the model was evaluated using 11 eddy covariance flux towers in various land cover types (i.e., savannas, woody savannas, croplands, evergreen broadleaf forests, and open shrublands), correcting for the energy balance closure (EBC). The Bowen ratio (BR) and residual (RES) methods were used for enforcing the EBC in the EC observations. The modeled ET was evaluated against unclosed ET and closed ET (ETBR and ETRES) under clear-sky and all-sky observations as well as gap-filled data. The results showed that the modeled ET presented a better agreement with closed ET compared to unclosed ET in both Terra and Aqua datasets. Additionally, although the model overestimated ETd across all different land cover types, it successfully captured the spatiotemporal variability in ET. After the gap-filling, the total number of days compared with flux measurements increased substantially, from 13,761 to 19,265 for Terra and from 13,329 to 19,265 for Aqua. The overall mean results including clear-sky and all-sky observations as well as gap-filled data with the Aqua dataset showed the lowest errors with ETRES, by a mean bias error (MBE) of 0.96 mm.day−1, an average mean root square (RMSE) of 1.47 mm.day−1, and a correlation (r) value of 0.51. The equivalent figures for Terra were about 1.06 mm.day−1, 1.60 mm.day−1, and 0.52. Additionally, the result from the gap-filling model indicated small changes compared with the all-sky observations, which demonstrated that the modeling framework remained robust, even with the expanded days. Hence, the presented modeling framework can serve as a pathway for estimating daily remote sensing-based ET on regional scales. Furthermore, in terms of temporal trends, the intra-annual and inter-annual variability in ET can be used as indicators for monitoring crop stress and drought.