Atmospheric Science Letters最新文献

Global mean near surface temperature change is the key metric by which our warming climate is monitored and for which international climate policy is set. At the end of each year the Met Office issues a global mean temperature forecast for the coming year. Following on from the new record in 2023, we predict that 2024 will likely (76% chance) be a new record year with a 1-in-3 chance of exceeding 1.5°C above pre-industrial. Whilst a one-year temporary exceedance of 1.5°C would not constitute a breach of the Paris Agreement target, our forecast highlights how close we are now to this. Our 2024 forecast is primarily driven by the strong warming trend of +0.2°C/decade (1981–2023) and secondly by the lagged warming effect of a strong tropical Pacific El Niño event. We highlight that 2023 itself was significantly warmer than the Met Office DePreSys3 forecast, with much of this additional observed warming coming from the southern hemisphere, the cause of which requires further understanding.

The Northern Hemisphere winter of 2021/2022 exhibited a positive North Atlantic Oscillation (NAO) which led to largely mild and wet conditions for Northern Europe. A moderate La Niña in the tropical Pacific and a stronger than average stratospheric polar vortex together explained the observed anomalies over the winter. Winter 2021/2022 was well predicted in general by seasonal forecast systems. The ensemble mean indicated a positive winter NAO and the forecast spread of forecasts from the Met Office GloSea6 seasonal prediction system spanned the observed mean sea level pressure anomaly for the whole winter and the individual months. However, December showed the largest departure from the mean of the forecast which is consistent with evidence from previous work that early winter ENSO teleconnections are too weak in model predictions. Nevertheless, around one in four members captured the negative NAO pattern in December. The strong pressure gradient and positive NAO predicted for the latter part of the winter allowed successful warning of the possibility of above average storminess and strong winds which occurred in February 2022. This is potentially useful information for the energy sector who increasingly rely on wind power and the insurance industry for warning of storm damage.

This paper assesses the extended-range forecast skills of the extreme heat events (EHEs) over South China based on three subseasonal-to-seasonal models (European Centre for Medium-Range Weather Forecasts [ECMWF], National Centers for Environmental Prediction [NCEP], and China Meteorological Administration [CMA]). Overall, ECMWF has the best skill, NCEP the second and CMA the poorest. The predicting skills of EHEs depend on the predicting skills of relevant circulation. Cases studies (June 4–6, 1999, August 19–29, 2009, and August 3–5, 2010) show that the three models generally predict circulation anomalies weaker than observation, leading to the misses of some extreme heat days (EHDs). In these cases, ECMWF is able to well predict the influence of tropical circulation, capture the major characteristics of mid-latitude circulation but with a slower propagating speed. NCEP could capture the main signals of tropical (mid-latitude) circulation, but with slower propagating speed (slower propagating speed, deviated direction or more northward location). CMA might produce some EHDs but is derived from the circulation anomaly with the wrong origin or location. Therefore, ECMWF could predict the EHEs most accurately, NCEP could reasonably predict the formation of EHEs and tend to have more delayed predictions, while CMA has the poorest skill due to the false origins of anomalies. These results suggest potential ways to improve the current models' extended-range forecast skills.

This study explores Arctic warming's effect on Eurasia's temperature variability, notably the warm Arctic–cold Eurasia (WACE) pattern, and assesses seasonal prediction models' accuracy in capturing this phenomenon and its monthly variation. Arctic warming events are categorized into deep Arctic warming (DAW), shallow Arctic warming (SAW), warming aloft (WA), and no Arctic warming (NOAW), based on the temperatures at 2 m and 500 hPa in the Barents–Kara Sea. It is revealed that DAW events are significantly correlated with monthly cold temperature anomalies in East Asia, predominantly occurring in January–February, excluding December. This study evaluates two primary capabilities of seasonal prediction models: their proficiency in forecasting these Arctic warming events, particularly DAW, and their ability to replicate the spatial patterns associated with DAW. Some models demonstrated notable predictive skill for DAW events, with enhanced performance in January and February. Regarding spatial pattern reproduction, models showed limited alignment with the reference dataset over the Northern Hemisphere (above 25° N) in December, whereas a higher degree of concordance was observed in January–February. This indicates their capability in capturing the atmospheric circulation patterns associated with DAW, pointing to areas where model performance can be enhanced.

Heavy precipitation is a major natural hazard in the Alps. Understanding the possible changes due to climate change is a prerequisite for effective climate adaptation and protection. In this study, we revisit the long-term (1901–2023) evolution of daily and multi-day heavy precipitation intensity and frequency, discuss trends for sub-daily to multi-day events in the recent period 1981–2023 and investigate elevation dependencies in the complex topography of Switzerland. We analyze station measurements from MeteoSwiss' dense operational network covering the whole country and a wide range of elevation levels. We find that daily maximum precipitation and the frequency of precipitation events exceeding the 99th all-day percentile have increased since 1901 with a peak in the 1980s and decreases thereafter. For the recent period 1981–2023, positive trends in summer heavy precipitation intensity are detected for short (10-min to 3-h) events, but no changes are found for the frequency of these moderate extreme events. For longer (1- to 5-day) events on the other hand, decreases in intensity and frequency are found, especially for the winter half-year. We hypothesize that the opposing trends on the centennial (1901–2023) vs. decadal (1981–2023) time scales are caused by the interaction between thermodynamics, reflecting the primary influence of human-induced climate change, and the internal variability of atmospheric dynamics. Moreover, we observe a small negative elevation dependency of the daily long-term trends up to 2300 m above sea level. For the 1981–2023 trends, no strong elevation dependencies are found for sub-daily events. For daily events, we find small opposing negative summer and positive winter elevation dependencies for both intensities and frequencies. The reason for these tendencies remains unclear. Our results underscore the need to further investigate the interplay between climate change, internal variability of large-scale dynamics and elevation to better understand heavy precipitation variability in the complex Alpine terrain.

This study investigates long-term trends related to the mature stage duration of tropical cyclones (TCs) over the western North Pacific (WNP) during June–November between 1982 and 2021. There is a significant shortening in the mean duration of the WNP mature TC stage, which is defined as the period when a TC is within 5 kt of its lifetime maximum intensity. This shortening is induced by a significant (weak) poleward migration in the starting (ending) location of the WNP mature TC stage, which can be further explained by changes in environmental conditions. From 1982 to 2021, there have been significant increases in maximum potential intensity and 700–500-hPa relative humidity over most of the WNP, which has broadly expanded the TC-favorable area poleward. Consequently, WNP TCs can start their mature stages and reach their lifetime maximum intensities at higher latitudes. By contrast, there are only weak changes in 850-hPa relative vorticity and 850–200-hPa vertical wind shear (VWS). Given the dominant role of VWS in modulating the TC weakening rate, the TC-suppressive area over the subtropical WNP has shown lesser changes, thus leading to insignificant changes in the ending location of the mature TC stage.

Reanalyses are utilized for calculating climatological trends due to their focus on temporal consistency. ERA5 reanalysis family has proven to be a valuable and widely used product for trend extraction. This study specifically examines long-term trends in total annual precipitation across two climatic hotspots: the Alps and Italy. It is acknowledged by reanalysis producers that variations in the observational systems used for data assimilation impact water cycle components like precipitation. This understanding highlights the need of assessing to what extent temporal variations in ERA5 precipitation amounts are solely a result of climate variations and the influence of changes in the observational system impacting simulation accuracy. Our research examines the differences between ERA5 and similar reanalyses against homogenized, trend-focused observational datasets. We find that discerning the climatological signal within ERA5 adjustments for observational system variations is challenging. The trend in ERA5 from 1940 to 1970 shows distinct patterns over the Alps and, to a lesser extent, Italy, diverging from later ERA5 trends and those in other reanalyses. Notably, ERA5 shows an increasing, although nonlinear, trend in the deviation between ERA5 and the observational datasets. Improving future reanalysis interpretability could involve adopting a model-only integration for the same period, akin to the ERA-20C and ERA-20CM approach.

The Aeroclipper is a new balloon device that can be attracted and captured by tropical cyclones (TC) and perform continuous in situ measurements at the air–sea interfaces. To estimate the potential effect of Aeroclipper observations on the analysis of TCs, virtual Aeroclipper observations targeting TC Haima (October 2016) were synthesized using an idealized surface pressure distribution and best track data and were assimilated using an ensemble data assimilation system. Results show that the assimilation of Aeroclipper measurements may provide a more accurate representation of the TC pressure, wind, and temperature in analyses. This also leads to improved precipitation around the Philippines. The ensemble spread shows that the Aeroclipper measurement assimilation has an impact on the analyses that extends into the tropics from the early stages of TC development. These impact signals propagate westward with easterly waves and eastward with large-scale convective disturbances. Although the underlying mechanisms need to be further examined and tested using real Aeroclipper measurements, the present study shows that these balloons could provide valuable observations to improve the precision of analyses in presence of a TC. This is a first step toward a study of the impact of the Aeroclipper measurement on TC forecast.

This study assesses the midsummer precipitation prediction over eastern China by the dynamic downscaling method. Based on the Climate Forecast System version 2 of the National Centers for Environmental Prediction and the Weather Research and Forecasting Model, the prediction performance of global and regional models on the July precipitation over eastern China is further analyzed by hindcast experiments from 1982 to 2010 and prediction experiments from 2011 to 2021. The results suggest that the regional model forced by the global model can noticeably improve the prediction skill for precipitation in eastern China, especially in the region from the South of North China to the Yangtze River Basin, referred as the northern China in this paper. In addition, we perform a diagnostic analysis of the reason for the improvement of the model prediction skill. The results indicate that the high resolution of the regional model and the refinement of physical process parameterizations contribute to improving the simulation ability for the East Asian atmospheric circulation pattern, heat flux, especially for the meridional teleconnection pattern in East Asia and the sensible heat flux in the northern China, thus further improving precipitation prediction.

The UK Met Office state-of-the-art, deterministic, convection-permitting, coupled land-atmosphere, regional weather forecasting system, known as the UKV or UK Variable resolution model (Tang et al. Meteorological Applications, 2013; 20:417–426), has been operational since 2015. Science updates are regularly made to the UKV land surface data assimilation scheme when those updates improve predictions of screen temperature and humidity, since these quantities have a direct impact on atmospheric states and weather forecasts. Less attention has been paid to whether UKV soil moisture analyses are close to independent, in-situ soil moisture observations, partly because it is difficult to make meaningful comparisons between 1.5 km² gridded model outputs and traditional point sensor measurements. Soil moisture is recognized to be important when hydrological forecasts for runoff and rivers are required. This is because soil moisture controls the extent to which rainfall can infiltrate the soil, and the amount of surface runoff affects the timing of peak river flows (Ward & Robinson, Principles of Hydrology. McGraw-Hill Publishing Company; 2000; Singh et al. Water Resources Research, 2021, 57, e2020WR028827). Gómez et al. (Remote Sensing, 2020; 12:3691) report benefits to river flow forecasts when using soil moisture data assimilation in the UKV system instead of a daily downscaled product from the Met Office global model. The Met Office measures soil temperature and soil moisture at Cardington (Osborne & Weedon, Journal of Hydrometeorology, 2021, 22:279–295); there is no other UK Met Office site at which soil moisture is measured. In this study, we use field-scale (~200 m radius) soil moisture measurements from the UK Centre for Ecology and Hydrology's (UKCEH's) COSMOS-UK network to provide independent verification and analysis of UKV soil moisture during summer 2018, an unusually dry period in the United Kingdom. We find that the match to COSMOS-UK soil moisture observations is generally good, and that changes made to the land data assimilation approach during a recent operational upgrade had a generally beneficial impact on UKV soil moisture analyses under very dry conditions.