International Journal of Climatology最新文献

Central Asia (CA) is among the world's most vulnerable regions to climate change. Increasing anthropogenic greenhouse gas concentrations (GHGs) are the primary forcing of the current and future climate system for the time scale of a century. By analysing observation datasets, we show that a warming of 1.2°C led to a decrease of 20% in snow-depth CA during the last 70 years, especially over the mountains. In recent decades, longer summer times and fewer icing days (more than 20 days·year⁻¹) have exposed unprecedented shock to CA's climate system's components. Furthermore, we analyse 442 model simulations from Coupled Model Inter-comparison Project Phase 5 and 6 (CMIP5, CMIP6) and show that CMIP6 simulations are generally warmer and wetter than the CMIP5 ones in CA. For instance, under the highest emission scenarios (RCP8.5 and SSP5-8.5), CMIP6 projects a 6.1°C increase, while CMIP5 projects a 5.3°C increase, suggesting CMIP6 anticipates greater warming with high emissions. In contrast to CMIP6, the CMIP5 precipitation trends suggest a potential nonlinear relationship between increased greenhouse gas emissions and changes in precipitation, though the impact is much less pronounced than the temperature changes. Our analysis shows that CMIP6 models are more sensitive to temperature rise than CMIP5 ones. Both simulation sets' ensemble means capture well the observed warming trend. The imposed snow-melting leads to an increase in the run-off in the vicinity of glaciers. Such climatic shifts lead to more flooding events in CA. Given the projected warming range of 2–6°C in CA at the end of the century in various scenarios and models, such warming trends might be catastrophic in this region. The seasonal cycle of the temperature change indicates an extension of the glacier's melting period under future scenarios with fossil-fueled development. The models' uncertainty increases for the far-future time-slice, and warming larger than 4°C in CA is very likely among all the models and during all the seasons if no sustainable action is taken. This study also incorporates a detailed Köppen climate classification analysis, revealing significant shifts towards warmer climate categories in Central Asia, which may have profound implications for regional hydrological cycles and water resource management, particularly in the Amu Darya and Syr Darya river basins under warmer scenario by the end of the century. The Tundra and ice cap climate categories will lose more than 60% of their coverage at the end of the century compared to the historical period in the Amu Darya and Syr Darya river basins.

Based on three large ensemble (LE) historical simulations, this study explored the contribution of low-frequency internal variability (IV) modes to global mean sea surface temperature anomaly (GmSST) during 1920–2010. The dominant contributions of low-frequency IV modes to GmSST in the three large ensembles are quite different. The low-frequency IV modes in the Indo-Pacific Ocean (i.e., IPO and IOBW) dominate the low-frequency GmSST signal for the CESM2 and MPI LEs (the ensemble median contribution value reaches 70%–80%), while the low-frequency IV modes in the North Atlantic dominate for the FGOALS-g3 LE (contribute more than 50%). CESM2 LE and MPI LE can reasonably reproduce the impact of low-frequency modes in the Indo-Pacific ocean as observed, but FGOALS-g3 LE underestimates this impact and overestimates the extratropical impact (on GmSST) of the North Atlantic. After removing the external forcing, it still retains the excessive signs in low-frequency IV modes in FGOALS-g3 LE over the North Atlantic. Furthermore, we used a pattern adjustment approach to revise the surplus effect in the North Atlantic. After revision, the contribution of decadal IVs in the North Atlantic to GmSST is reduced by 30%. Meanwhile, the tropical contribution in the Indo-Pacific Ocean is increased and that is closer to the observed one. This approach can be employed to revise the weak or strong IV signals in other regions or LEs, which is meaningful for reducing the uncertainty of IV signals.

The precipitation trend patterns in South America (SA) are determined using trend empirical orthogonal function analysis for the 1951–2016 period. The associated large-scale tropical and extratropical anomalous circulation patterns are also examined. The words “total” and “residual” refer to the monthly anomalies and monthly anomalies without the El Niño–Southern Oscillation (ENSO) effects, respectively. The total precipitation features a positive trend in southeastern SA (SESA, southern Brazil, Uruguay, most of eastern Argentina) and northern Chile, and a negative trend over central-eastern Brazil and central Amazonia. The residual precipitation shows an increased positive trend over most of the coastal extension of northern SA and Colombia; a weak positive trend over southern Brazil, northeastern Argentina, and northern Chile; and a negative trend over central-eastern SA and western Amazonia. The differences between the total and residual precipitation trend patterns in tropical SA is explained as responses to total and residual zonally asymmetric anomalous sea surface temperature (SST) patterns, respectively. The total SST pattern along the equatorial Pacific configures the Pacific Decadal Oscillation, which impacts ENSO variability and as response intensifies the Walker circulation. Without the ENSO, the Walker cell is mainly driven by the tropical Indian and Atlantic Oceans, which configure residual asymmetric anomalous warming. Furthermore, the warming in the equatorial Indian and eastern Pacific Oceans, in the presence of ENSO, induces a Rossby wave train-type anomalous pattern that extends across the South Pacific into SA and modulates the atmospheric anomalous circulation over SESA. In this region, an anomalous anticyclonic accompanied by an intensified South American Low-Level Jet induces a moisture transport to SESA. This anticyclone is also observed in the absence of ENSO but is weaker. The results suggest the importance of ocean warming in the western Pacific-Indian in the modulation of extratropical teleconnections to SESA in the tropical ocean warming scenario.

The North China Plain, a crucial region for winter wheat cultivation, exhibits yield deeply affected by the variability of winter precipitation. This study examines the interdecadal variation in the relationship between the North Pacific Oscillation (NPO) and winter precipitation in the North China Plain (WPNC). Utilizing the East Asian winter monsoon (EAWM) as an intermediary, we have observed an interdecadal variation in the relationship between WPNC and NPO after the late-1990s. Before 1994, the relationship between NPO and EAWM/WPNC both exhibited a significant positive correlation, while after 1998, their correlation decreased and became insignificant. This interdecadal variation can be attributed to the eastward shift of the winter NPO's location after the late-1990s. Our investigation found that the eastward shift of the NPO's location is closely linked to the phase transition of the Atlantic Multidecadal Oscillation (AMO) phase after the late-1990s. The warm sea surface temperatures (SST) over the North Atlantic cause ascending motion, and the outflows induce a compensatory anticyclonic circulation over the North Pacific. The easterlies anomaly on the south side of the anticyclone weakens climatological westerlies, increasing SST and upper-level air temperatures in the North Pacific through the wind–evaporation–SST–longwave radiation effect. The resulting warmer air strengthens the atmospheric temperature gradient, enhancing the vertical integration of baroclinic energy conversion and shifting the NPO eastward, reducing its correlation with WPNC.

Accurate precipitation records are an essential component when monitoring the climate and studying its changes. However, analysis is typically limited by the large quantities of missing values present. This article proposes two new imputation techniques for incomplete monthly data collected from a rainfall monitoring network in the Republic of Ireland from 1981 to 2010. The data considered is high-dimensional due to the large number of over 1100 rain gauge stations present, and the methods presented are designed to handle such cases. These are Elastic-Net Chained Equations (ENCE) and Multiple Imputation by Chained Equations with Direct use of Regularized Regression by elastic-net (MICE DURR). Both methods predict missing data by a series of regularized regression models, where MICE DURR differs from ENCE by also using multiple imputation. Through various evaluations across different levels of missingness, ENCE and MICE DURR consistently outperformed existing imputation methods in terms of RMSE and $��R^2�$. Moreover, they have provided the best results both seasonally and for accurately predicting extreme values. An RMSE of 14.16 and 14.17 mm per month were reported for ENCE and MICE DURR, respectively, when stations that were at least 50% complete during the study period were included. For increasingly sparser data, the imputation accuracy achieved from MICE DURR surpasses ENCE, demonstrating the efficacy of multiple imputation when handling a substantial amount of missing data. Validation metrics indicate that these methods compare very favourably to existing methods in the literature, such as those that use random forests or multiple linear regression.

Trends in temperature variability are often referred to have higher effect on temperature extremes than trends in the mean. We investigate trends in three complementary measures of intraseasonal temperature variability: (a) standard deviation of mean daily temperature (SD), (b) mean absolute value of day-to-day temperature change (DTD) and (c) 1-day lagged temporal autocorrelation of temperature (LAG). It is a well-established fact that different types of data (station, gridded, reanalyses) provide different temperature characteristics and particularly their trends. Moreover, we have uncovered that trends in measures of variability are considerably sensitive to data inhomogeneities. Therefore, we use five different datasets, one station based (ECA&D), one gridded (EOBS) and three reanalyses (JRA-55, NCEP/NCAR, 20CR), and compare them. The period from 1961 to 2014 where all datasets overlap is examined, and the linear regression method is utilized to calculate trends of investigated measures in summer and winter. Intraseasonal temperature variability tends to decrease in winter, especially in eastern and northern Europe, where trends below −7%·decade⁻¹ are detected for all measures. Decreases in DTD and LAG (indicating increase in persistence) prevail also in summer while summer SD tends to increase. The increase in the width of temperature distribution and the simultaneous increase in persistence indicate a tendency towards the rise in the frequency of extended extreme events in summer. Unlike previous studies, our results imply that reanalyses are not the least accurate in determining trends. JRA-55 appears to be the least diverging from other datasets, while the largest discrepancies are detected for DTD at station data.

This paper details the creation of a tropical cyclone (TC) size dataset using the NCEP/NCAR Reanalysis I dataset for landfalling TCs along the United States coastline from 1948 to 2022. The radius of the outermost closed isobar (ROCI) is used as the size parameter. The dataset comprises landfall ROCI for 220 TCs. Storms are split into three zones (Texas–Alabama, Florida and Georgia–Maine) to determine if TC size varies geographically. Results showed a significant difference in landfall size, with Florida storms larger on average than the Texas–Alabama storms. Additionally, TC size increased with increasing intensity from tropical storm to Category 3, and storms tended to be larger later in the hurricane season, but there was no significant trend in landfall size over the 75-year period. ROCI exhibited statistically significant positive correlations with longitude and wind speed and a negative correlation with the outermost closed isobar's pressure. The dataset's creation is an example of how reanalysis datasets can be used to develop a TC size climatology.

This study demonstrates asymmetric relationships between El Niño–Southern Oscillation (ENSO) and tropical cyclones (TCs) affecting the Philippines during October–December. In El Niño or La Niña years, the number of TCs impacting the Philippines may increase or decrease. These variations result in four ENSO–TC variability types all of which exhibit strong sea surface temperature (SST) anomalies across the equatorial eastern Pacific. The major difference between the active and inactive types in terms of El Niño or La Niña years is related to the magnitude of SST anomalies in the tropical western Pacific (TWP) over the 120°–150°E region. During El Niño years, moderate cold SST anomalies in this TWP region cause an anomalous divergent centre around the 120°–130°E zone to evoke an anomalous cyclone east of the Philippines. In the western North Pacific (WNP), this anomalous cyclone causes more TCs to form and move toward the Philippines, resulting in active TC activity. For the inactive TC type during El Niño years, very weak cold SST anomalies in the aforementioned TWP region correspond with a northeastward-extended anomalous divergent centre over the 120°–140°E, 10°S–20°N zone and an anomalous anticyclone across the Philippines and its eastern side. Decreases in the formation of the WNP TC and movement toward the Philippines lead to inactive TC activity. The large-scale anomalies and regulating processes are mainly opposite between the active TC type during El Niño years and the inactive TC type during La Niña years. These two types are influenced by interdecadal variability of the Pacific decadal oscillation. Opposite anomalies and regulating processes also occur between the inactive TC type during El Niño years and the active TC type during La Niña years. The former type is jointly modulated by the positive Indian Ocean Dipole mode and central-Pacific El Niño.

In this study, a new skill score (SS) is proposed to evaluate the performance of climatological East Asian summer precipitation (EASP) in the Coupled Model Intercomparison Project Phase 6 (CMIP6) over the historical period. By applying the empirical orthogonal function (EOF) to the EASP bias of CMIP6 models, the intermodel spread of EASP bias is revealed to be dominated by the first two modes: the uniform precipitation bias pattern and the north–south dipole precipitation bias pattern. Then the SS is constructed by the weighted-average model-observation distances regarding different EOF modes, where the model-observation distance in a certain EOF mode is defined as the difference between their principal components, and the weight is the corresponding percentage variance. The perfect-models ensemble based on the SS shows a spatial magnitude close to the observation, indicating that the SS effectively depicts the models' historical performance. However, no robust relationship is found between the model's historical performance and future projection regarding the EASP. This is because they are governed by different physical factors. The historical EASM is determined by the thermal responses to a specific radiative forcing, while the future change in EASP is associated with the warming rate along with the increased radiative forcing.

To minimize the risks from tropical cyclones (TCs), the quantification and regular monitoring of TC activities are strongly needed. While the accumulated cyclone energy (ACE) (Bell et al., 2000, Climate assessment for 1999. Bulletin of the American Meteorological Society, 81, S1–S50) has been widely used for examining the level of basin-wide TC activities, the conventional ACE does not discriminate the regional characteristics of TC risks. By introducing a point-wise version of ACE, this paper proposes a geographical approach to the risk map for TCs. Here, the alternative metric is named the localized ACE (LACE), which interprets the TC risk directly felt by the local residents. Annual LACE at a geographical point measures TC activity by merging the quantities of frequency, intensity and duration factors, which contribute to the local TC activity in a year. In conjunction with LACE, a concept of LACE partial contribution (LACEP) is also proposed. The LACEP enables quantitative comparison of the contribution by each factor to the LACE, and thereby identifies the characteristics of the regional TC risks. To demonstrate the efficacy of the indices, that is, LACE and LACEP, this paper provides the response of the local TC activities to El Niño–Southern Oscillation in the western North Pacific and confirms the value of these indices.