International Journal of Climatology最新文献

Moisture sources and transport processes play a critical part in hydrological cycle and determine regional precipitation. This paper utilizes the Water Accounting Model-2layers (WAM-2layers) and the ERA5 reanalysis data to track the sources of precipitation over the Pearl River Basin (PRB). The contribution of external moisture and the role of local recycling are investigated. The results show that during the period from 1980 to 2020, oceanic sources including the western North Pacific and Indian Oceans serve as the primary moisture sources of precipitation over the PRB. The contributions to total seasonal precipitation are respectively 62.57% in MAM, 54.79% in JJA, 43.70% in SON and 60.88% in DJF. By contrast, the contribution of local recycling is generally below 5.50%. In the dry years of 1994, 1997 and 2001, the contribution of terrestrial sources is about 19.22%; in the wet years of 1989, 2009 and 2011, the contribution is about 16.31%. The summer precipitation anomalies are mainly attributable to moisture anomalies from the Equatorial Indian Ocean in the wet years and from Southeast Asia in the dry years. Furthermore, vertically integrated moisture flux anomalies over the boundaries of the PRB are generally the result of anomalous wind rather than anomalous moisture. In the wet years, low-pressure systems induce strong cyclonic moisture transports, increasing the PRB precipitation. In the dry years, high-pressure anomalies over the PRB block the moisture transports from the Indian Ocean and western North Pacific.

Reanalysis datasets provide a continuous picture of the past climate for every point on Earth. They are especially useful in areas with few direct observations, such as Siberia. However, to ensure these datasets are sufficiently accurate they need to be validated against readings from meteorological stations. Here, we analyse how values of six climate variables—the minimum, mean and maximum 2-metre air temperature, snow depth (SD), total precipitation and wind speed (WSP)—from three reanalysis datasets—ERA-Interim, ERA5 and ERA5-Land—compare against observations from 29 meteorological stations across Siberia and the Russian Far East on a daily timescale from 1979 to 2019. All three reanalyses produce values of the mean and maximum daily 2-metre air temperature that are close to those observed, with the average absolute bias not exceeding 1.54°C. However, care should be taken for the minimum 2-metre air temperature during the summer months—there are nine stations where correlation values are <0.60 due to inadequate night-time cooling. The reanalysis values of SD are generally close to those observed after 1992, especially ERA5, when data from some of the meteorological stations began to be assimilated, but the reanalysis SD should be used with caution (if at all) before 1992 as the lack of assimilation leads to large overestimations. For low daily precipitation values the reanalyses provide good approximations, however they struggle to attain the extreme high values. Similarly, for the 10-metre WSP; the reanalyses perform well with speeds up to 2.5 ms⁻¹ but struggle with those above 5.0 ms⁻¹. For these variables, we recommend using ERA5 over ERA-Interim and ERA5-Land in future research. ERA5 shows minor improvements over ERA-Interim, and, despite an increased spatial resolution, there is no advantage to using ERA5-Land.

Three out of the five highest daily winter rainfall totals on record over Northern England have occurred from 2015 onwards. Heavy rainfall events in the winters of 2013–2014, 2015–2016 and 2019–2020 led to more than 2.8-billion-pounds of insurance losses from flooding in the UK. Has the frequency of these events been influenced by human-induced climate change? Winter rainfall in the UK is extremely variable year-to-year, which makes the attribution of rainfall extremes particularly challenging. To tackle this problem, we introduce an UNprecedented Simulated Extreme Ensemble (UNSEEN) approach for the attribution of such extremes, thereby increasing the data available, and apply this approach to five recent flooding events on a regional scale. Using this method, for all five events we found a significant climate signal in the extreme regional rainfall totals immediately preceding the flooding. Results were fairly similar for each—with the events being found to become from 1.4 to 2.6 times more likely. An alternative attribution method that uses a different model with substantially less data did not find significant increases, reinforcing the need for very large amounts of data to detect significant changes in extreme rainfall against a noisy background of natural variability. We also examine how extreme rainfall is changing more broadly across English regions in winter, finding that 1-in-10 to 1-in-90-year winter rainfall totals have changed significantly in Northern England. The high volume of data using UNSEEN has enabled us to examine the dynamics of these events, showing that daily extremes in winter are likely to have increased across all the circulation patterns responsible for high rainfall in English regions.

The consensus on climate warming is well-established, and extreme values inherently encapsulate more information than averages. Against the backdrop of frequent extreme climate events, studying extreme values holds profound significance. This study aims to reveal the characteristics of extreme climate events and their role in triggering extreme hydrological events in the typical North China basin, that is, the Luanhe River Basin. Trends of 25 extreme climate indices during 1960–2018 are analysed using the Sen's slope and MK significance test to study the changing characteristics of extreme climate. Characteristics of extreme flood and dry events are examined, encompassing trend analyses at different time scales (seasonal, interannual, decadal) and concentration analysis. Finally, and most significantly, correlation analysis is conducted on extreme climate indices and features of extreme hydrological events, followed by principal component analysis of extreme climate indices, to precisely quantify the impact of extreme climate on the occurrence of extreme hydrological events. The results indicate a warming trend in extreme temperature indices, with a more significant rise in minimum temperatures compared to maximum temperatures. There is a significant decrease in precipitation, but precipitation at higher magnitudes is less affected by the overall reduction in total precipitation. Extreme dry events have markedly increased, particularly concentrated in winter with delayed occurrences, primarily induced by extreme temperature events, that is, warming effects. Conversely, extreme flood events have significantly decreased, mainly concentrated in summer and early autumn, predominantly caused by extreme precipitation and extreme high-temperature events. The climate and hydrological conditions in the study area have become more extreme and complex. Severe river droughts may occur more frequently in winter, while extreme flooding may happen in summer. Therefore, it is necessary to pay more attention to these developments.

A homogenized daily mean temperature series from 1915 to 2021 at Beijing Observatory (BO) has been developed. A thorough investigation of observation records and historical metadata was carried out to identify specific non-climatic biases. The inhomogeneities were detected and adjusted with the optimally selected reference stations using a statistical reduction methodology. The results indicated three types of non-climatic biases in temperature records: the first was the relocation of BO from suburban to urban, which caused positive shifts in temperature records, especially for wintertime; the second arose from different methods for calculating daily mean temperature, which caused different sign biases varying with months; and the last was caused by the transition from manual to automatic measurements with site-specific biases. The corresponding adjustments due to three type biases ranged from −1.13 to 0.63, −0.29 to 0.23 and −0.13 to 0.00°C, respectively. The new homogenized annual mean temperature series showed a warming trend of 0.199°C/decade during 1915–2021. The trend was more pronounced in winter than in the other three seasons. The warming trend in the new series is greater than those in the previous homogenized series (0.136–0.177°C/decade), primarily due to the more effective adjustment associated with the site relocation, particularly for more urban locations.

The frequency and power dissipation index (PDI) of tropical cyclones (TCs) in the North Indian Ocean (NIO) has dramatically grown over the last 10 years, according to our analysis utilizing the European Centre for Medium Range Weather Forecasts reanalysis version-5 (ERA5) dataset and the India Meteorological Department (IMD) best track data over the period 1982–2021. Our findings indicate that the recent increase in the post-monsoon TC PDI over NIO is caused by a reduction in wind shear and an increase in convective available potential energy over the Bay of Bengal. The importance of improving our understanding and developing more accurate predictions of the TCs activity has increased as TCs become more frequent and intense owing to the effects of global warming. This study therefore develops TC prediction models for frequency and PDI over NIO for various lead times that vary from 2 to 8 months in addition to the trend analysis employing the ERA5 and IMD best track data. The potential predictors used in proposed model are surface temperature (2 m temperature over land and sea surface temperature over oceanic regions), vertical wind shear, winds (zonal, meridional), geopotential height and temperature at different pressure levels during January–August. The developed models are remarkably accurate (skill is ~94%) in forecasting TCs frequency and PDI. The proposed models outperform the best performing models that are already in use for long-range TC activity prediction. With a lead-time as long as up to 8 months, this effort is the first to investigate the possibility of forecasting the frequency and PDI of post-monsoon TC activity over the NIO with good accuracy. Therefore, the developed models show an operational potential for seasonal TC activity forecast over the NIO.

The relationship between precipitation and cloud properties in Southwest China are investigated by using the CLARA-A2 cloud parameters data and TRMM-3B43 precipitation data from 1998 to 2015. Ice water path (IWP) and cloud top height (CTH) are significantly and positively correlated with precipitation in all regions, indicating that ice-phase processes and cloud development processes are the critical processes influencing precipitation. Precipitation is also directly associated with cloud fractional coverage (CFC) due to the significant positive correlation between CFC and precipitation in all regions except the Sichuan Basin (SCB). A positive correlation between liquid water path (LWP) and precipitation is found in the Eastern Tibetan Plateau (ETP) and Yunnan-Kweichow Plateau (YKP), but not in the Western Tibetan Plateau (WTP) and SCB. Notably, the response of precipitation to LWP is not as good as that to IWP in SCB. Precipitation is significantly negatively correlated with ice effective radius (IREF) in WTP and ETP and positively correlated with liquid effective radius (LREF) in ETP, YKP and SCB. IREF and LREF are closely related to cloud microphysical processes. Specifically, small IREF could accelerate the Bergeron process and thus increase precipitation, while large LREF is closely related to the cloud droplets coalescence process. Results indicate that the difference in precipitation between the cold and warm seasons is related to convective available potential energy (CAPE) and low troposphere relative humidity (RH). High CAPE and RH favour the precipitation occurrence in Southwest China. The influence of CAPE and RH on precipitation is more significant in the ETP than that in the WTP, owing to the orographic lifting and moisture transport from the Indian Ocean. Thermodynamic and humidity conditions have a greater impact on precipitation by influencing LREF, LWP and IWP in YKP. In SCB, precipitation shows a strong dependence on CAPE, IWP and LREF, but not on RH.

Heat stress and flood impacts have been extensively studied separately because of their significant societal and economic impacts, albeit apart from each other. Here we show that heat stress can trigger floods across large areas of North and South America, southern Africa, Asia and eastern Australia. We also show that the compounding of heat stress and floods is projected to worsen under climate change. This effect is magnified as we move from the Shared Socioeconomic Pathways (SSPs) 1–2.6 to 5–8.5. Moreover, in the future, the compounding between heat stress and floods is projected to extend to Europe and Russia, two areas where it has not been identified as relevant in the past. Moreover, by intersecting our results with future projections of the population of urban agglomerations, we find that heat stress/flood compound can pose a serious risk to a large portion of the world population. These results highlight the need towards improved preparation and mitigation measures that account for the compound nature of heat stress and flooding, and how the compounding is expected to be exacerbated because of climate change.

Under global warming, the summer surface air temperature (SAT) change signal in the northern mid-latitudes is generally the most significant, while the SAT change in North China (NC) shows distinct local characteristics. Using simulations from the Coupled Model Intercomparison Project Phase 6 and observation from Berkeley Earth Surface Temperature, this study reveals an unusually weak signal of summer SAT change in NC since the 1960s. This uniquely weak signal can be attributed to the small net contribution of external forcings, mainly from anthropogenic greenhouse gases (GHG) and aerosols (AA). Compared to other land regions in the same latitudinal band, the GHG-induced warming was weaker and AA-induced cooling was stronger in NC, resulting in the weakest SAT change signal and thus the lowest signal-to-noise ratio. This weaker GHG-induced warming plays a more important role in the SAT change difference between NC and other land regions in the same latitudinal zone. Under different emission scenarios in the future, the signal-to-noise ratio in NC will become as large as those in the other land regions in the same latitudinal band and the northern hemisphere, which is partly due to the smaller relative differences in the SAT change signal between NC and the other two regions. The projections of SAT change indicate that climate change in NC will probably become more violent and vulnerable.

Many segmentation or change-point detection methods for homogenizing climate time series compare candidate station data with reference data to eliminate common climate signals and more efficiently detect spurious, non-climatic changes. One drawback is that it is difficult to decide whether the detected change-point is due to the candidate series or to the reference. A so-called attribution procedure is typically applied in a post-processing step for each detected change-point. This article describes a new statistical method for the attribution of change-points detected in Global Navigation Satellite System (GNSS) minus reanalysis series of integrated water vapour. It requires at least one nearby station with similar GNSS and reanalysis data. Six series of differences are formed from the four base series (BS) and are tested for a significant jump at the time of the change-point detected in the candidate station. The six test results are analysed with a statistical predictive rule to attribute the change-point to one, or several, of the four BS. Original aspects of our method are: (1) the significance test, which is based on a generalized linear regression approach, taking both heteroscedasticity and autocorrelation into account; (2) the predictive rule, which uses a machine learning method and is constructed from the test results obtained with the real data by using a resampling strategy. Four popular machine learning methods have been compared using cross-validation and the best one was applied to a real data set (49 main stations with 114 change-points). The results depend on the choice of the test significance level and the aggregation method combining the prediction results when several nearby stations are available. We find that 62% of the change-points are attributed to GNSS, 19% to the reanalysis, and 10% are due to coincident detections.