International Journal of Climatology最新文献

Climate change in India is causing devastating downpour events and shifts in spatial characterization, with apparent regional differences. The Northwest India (NWI), which is in the Aravalli rain shadow zone and was formerly drier, has attracted great attention in recent years due to its changing rainfall patterns. This study throws light on the astonishing behaviour of ISMR over NWI with the advent of intensified rainfall over the region in the recent time frame. The Pettitt test of change point (CP) detection is utilized in this analysis to measure the change in rainfall patterns over a period of 70 years, from 1950 to 2019. This analysis suggests significant variation in the CP's time frame for different months of ISMR. The earliest change was noticed in July, while the latest was for September for the mean as well as for the intense precipitation. Moreover, we found a maximum change in the precipitation during the peak monsoon month (i.e., July and August). The difference in the precipitation of different percentile values before and after CP indicates a decrease (increase) in low (high) intensity precipitation for all the months and seasons as a whole with varying magnitude. The highest reduction of low-intensity precipitation is noticed for the months of July and August, while the highest increase of high-intensity precipitation (>95th percentile) is noticed for June and September. The heavier precipitation contributes largely to the mean increase of precipitation, expecting to receive more mean and precipitation extremes in the future. Unlike the increasing precipitation trend, potential evapotranspiration (source of local moisture) shows a declining trend, revealing the negative association among them and the possibility of enhanced advection of remote moisture responsible for enhanced precipitation over NWI. The enhanced vertically integrated moisture transport of the Arabian Sea and Bay of Bengal and its strengthening relationship with precipitation further confirm the contribution of remote moisture to intensified precipitation over NWI, though the in-depth dynamical cause remains unclear. The increased Convective Available Potential Energy for entire monsoon seasons as a whole and individual months facilitates a favourable condition for enhanced convective activity over the region, resulting in strengthening the precipitation.

The out-of-phase mode of winter surface air temperature anomalies (SATAs) over northern Central Asia (NCA; 45°–65°N, 40°–100°E) between January and February is investigated in this study. This mode corresponds to warm (cold) SATAs in January (February) over NCA and is mainly modulated by the enhanced tropical convection anomalies over the Maritime Continent in previous late December, associated with MJO phase 4. These tropical convection anomalies can induce anomalous tropospheric Rossby-wave sources over the North Pacific in late December. The eastward-propagating Rossby-wave train, triggered by these anomalous Rossby-wave sources, can cause negative and positive tropospheric geopotential height anomalies over the Greenland–Scandinavia region and NCA in the following early–mid-January, subsequently leading to warm SATAs over NCA in January. The negative geopotential height anomalies over the Greenland–Scandinavia region in early–mid-January can trigger upward-propagating wave activity fluxes (WAFs) into the stratosphere, resulting in negative stratospheric geopotential height anomalies in late January–early February. These stratospheric anomalies, by triggering downward-propagating WAFs, can in turn lead to positive tropospheric geopotential height anomalies over the Greenland–Scandinavia region in early February. These anomalies over the Greenland–Scandinavia region can maintain themselves in the following mid- and late February by feedback of anomalous storm tracks, and cause negative geopotential height anomalies and subsequently cold SATAs over NCA in February by triggered southeastward-propagating Rossby-wave train.

Climate classification is a commonly used tool to simplify, visualize and make sense of an otherwise unwieldy amount of climate data in applied climate science research. Typically, these classifications have stemmed from one of two perspectives, either a circulation-to-environment (C2E) approach, or an environment-to-circulation approach (E2C), each with advantages and drawbacks. This research discusses a novel environment-to-circulation-to-environment (ECE) perspective to applied climate classification, and develops a specific ECE methodology that utilizes canonical correlation and discriminant analysis—the CANDECE method. The benefits of the ECE approach generally, and the CANDECE methodology specifically, are demonstrated in applying climate classification to aid in modelling anomalous water levels (AWLs) along portions of the East and West coasts of the United States. Results show that the CANDECE method performs better than two traditional classification methods (k-means and self-organizing maps [SOMs]) at relating AWLs to their broad-scale atmospheric setups, especially with regard to both high and low extreme AWLs. It is further demonstrated that, compared with the West coast, the CANDECE method is particularly advantageous along the southeastern US coast, where the primary modes of atmospheric variability (which drive the classifications produced by SOMs and k-means) do not align with the relevant circulation-based factors driving AWL variability. While AWLs were utilized for demonstrating the ECE proof-of-concept herein, ECE and CANDECE are designed to be useful for any climate application.

Three-hourly data from two satellite rainfall estimates products, PERSIANN and TMPA, are analysed to document the seasonal patterns of diurnal rainfall distribution over the Congo Basin and neighbouring areas. PERSIANN data for 2001–2017, at a one-hour time-scale, are further used to identify rain cells (≥4 mm·h⁻¹) in an attempt to explain the diurnal rainfall variations. Over land areas, an afternoon rainfall maximum is clearly shown, but over much of the region only a minor part of the rains (20%–30%) falls in the wettest 3-h period. Substantial rains (often 50%–60%) occur in the evening and at night, as a progressively delayed peak from east to west, but a seasonal change is found in the meridional propagation of the peak diurnal rainfall, in a south-westerly direction in January, and a north-westerly direction in July. Rain cells have prominent genesis areas west of high terrain, but can develop over most regions, with a peak genesis time slightly ahead the diurnal phase of the rains. The size, mean lifetime and mean rainfall intensity of the rain cells are strongly related to each other and display a semi-annual cycle not fully in phase with the seasonal cycle of the rains. The mean rain cell propagation speed (6.7 m·s⁻¹) is much lower than in previous studies, which focused on mesoscale convective systems. Rain cells which have a longer lifetime move much faster, the mean speed of those lasting less than 6 h being half that of those lasting at least 24 h. Most (86%) of the mobile rain cells propagate westward, but the meridional component of their propagation shows an annual cycle (southward in austral summer, northward in boreal summer) which matches the mid-tropospheric winds and explains the seasonal changes in the diurnal rainfall peak.

Climate change is expected to change the frequency, duration, intensity, and pattern of precipitation, underscoring the need for accurate predictive tools. Earth system models (ESMs) serve as invaluable instruments in this endeavour, simulating climate variable variations across temporal and spatial dimensions. This study aims to develop a methodology for generating precise daily precipitation maps by rectifying biases inherent in ESM outputs. The proposed methodology includes downscaling ESM outputs to simulate historical daily grid-based precipitation, thereby enhancing the fidelity of daily precipitation representation. For this purpose, 14 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were employed. Random forest (RF) machine learning method was used to correct biases in these ESM outputs. This study's novelty lies in integrating results of a grid-based RF classification model, employed to distinguish between rainy and non-rainy days, with those obtained by two RF regression models, to estimate precipitation amounts for grid cells receiving extreme and non-extreme precipitation, to generate an ensemble of ESM outputs. The resulting method, termed the triple coupling method (EN-RF), was validated using precipitation data from the Divandareh-Bijar Basin in western Iran to simulate historical climate conditions. Furthermore, the accuracy of the developed triple coupling approach was compared with that of a commonly used single machine learning-based downscaling model (EN-Single-RF). Comparative analysis against a commonly used single machine learning-based downscaling model (EN-Single-RF) revealed the superior performance of the EN-RF approach in replicating the intensity and distribution of daily precipitation. Furthermore, within the triple coupling framework, support vector machine (SVM) was utilized to simulate daily historical precipitation (EN-SVM), while the quantile mapping (QM) method served as a benchmark. Comparison of the results showed superiority of the EN-RF to other methods (EN-Single-RF, EN-SVM, and QM) in terms of various accuracy metrics (Kling-Gupta Efficiency = 0.95, mean square error = 0.22). The findings indicated the capability of the proposed triple coupling framework using the RF algorithm to simulate the spatio-temporal distribution of precipitation using the ESM precipitation outputs. The developed framework can be used to produce reliable projections to gain deeper insights into the potential impacts of climate change on regional precipitation patterns.

The rainfall regime is a critical factor in the Yucatan Peninsula, as the spatial and multiannual variability of rainfall is a major concern, particularly for crops. Variability in the rainy season was examined considering the onset and demise of the annual rainy season, the total rain volume, the rainfall season duration and the intense precipitation events recorded in meteorological stations (1978–2020). We analysed individual time series and calculated the long-term trend. Additionally, we explored the relationship between each summer rainfall characteristic and several oceanographic indices using multivariate techniques. We also developed a Trans-Isthmic Index from the relationship between the El Niño–Southern Oscillation and the Atlantic Multidecadal Oscillation. This index allows for determining the effect of the overall influence of the ocean on climate. The timeseries analysis revealed a high interannual variability and long-term positive trends concerning the duration of the rainy season with earlier onset and later demise, and the total rainfall volume and also a positive trend for the occurrence of heavy precipitation suggesting a shift in intra-annual patterns. Spatially, the analysis revealed clusters of stations with a similar variation, probably related to the AMO, NIÑO3.4 or TII indices. The spatial pattern was confirmed by analysing CHIRPS gridded precipitation data. Our results show that wetter conditions are associated with lower temperatures in the equatorial Pacific and warmer conditions in the Atlantic.

In this study, hindcasts from eight Copernicus Climate Change Service (C3S) and three North American Multi-Model Ensemble (NMME) operational seasonal forecast systems, based on dynamical climate models, are employed to investigate the influence of the South Atlantic Ocean Dipole (SAOD) on the predictive skill of Central Africa (CA) rainfall. The focus is primarily on the June–July–August season for 1993–2016. The findings reveal that, when regionally averaged, all models exhibit positive skill in predicting CA rainfall, except for the Geophysical Fluid Dynamics Laboratory (GFDL-SPEAR) model. Notably, there are significant spatial variations in skill across different regions. Model performance is particularly low (high) in the Central African Republic and Congo Basin (Gabon and Chad) and tends to deteriorate with increasing lead-time. Models that demonstrate a strong connection between SAOD and CA rainfall tend to exhibit better predictive skills in forecasting rainfall, in contrast to models with weaker connections. This leads to a significant in-phase relationship between the predictive skills of rainfall and the strength of the SAOD–rainfall connection among the models. Furthermore, the atmospheric circulation responding to SST forcing associated with the El Niño–Southern Oscillation exerts a significant influence on the robust atmospheric circulation associated with the climatological mean of SST over the SAO. This suggests that mean state bias in the SAO/equatorial Pacific region plays a role in modulating the strength of the simulated SAOD–CA rainfall connection and, consequently, the prediction skill of CA rainfall. In general, both NMME and C3S models appear to be valuable tools capable of providing essential seasonal information several months in advance. These insights can aid decision-makers in the region in making informed decisions regarding adaptation and mitigation measures.

This study investigates the characteristics of cold events affecting eastern China during November–March of 1979–2018 using station observations and the ERA5 reanalysis, focusing five major cities (Beijing, Zhengzhou, Wuhan, Shanghai and Guangzhou) for their high vulnerability and distinct local thermal conditions than the broader climate regimes. For each city, cold day (CD) (one or more continuous days where the temperature falls below the local 5th percentiles) and cold surge (CS) (a period of 1–3 days with a total temperature decrease exceeding 8°C) were both examined in aspects of occurrence frequency, circulation characteristics and health impacts. Results show that the northern cities are relatively more/less exposed to CD/CS than the southern cities. At all five cities, the two types of events rarely coincide and exhibit distinct multiple-year variations, as CD and CS are, respectively, dominated by continental- and regional-scale circulations. However, both types of events are associated with the interplay of the East Asia trough at 300 hPa and the Siberian high, Aleutian low and subtropical high at 850 hPa. Results also show that during CDs in these cities, the effective temperatures (ET), which take into account of the near-surface wind speed and humidity, are often about 5°C lower than the actual temperatures. The ET decreases are larger than the actual temperature drops in most CSs, yet in specific scenarios (primarily in Beijing and Zhengzhou when the temperature drop is relatively small), the ET drop can be less pronounced, as the possible decrease of wind speed and/or humidity can partially mitigate the ET decrease caused by dropping temperatures. These underline the complexity of health impacts of cold events, which vary regionally due to differences in covariations of temperature, wind speed and relative humidity. There aspects are worthy of further investigation.

Many previous studies have examined the influence of Arctic sea ice on the weather/climate of the Northern Hemisphere. However, the precursor signals of Arctic sea ice regarding East Asian winter low temperatures, which are important for climate prediction, have received little attention. This study identified an out-of-phase relationship between the autumn sea ice area of the Kara Sea (SICK) and subsequent winter minimum temperature in Northeast China (NEC) during 1979–2019, that is, diminished SICK facilitates cooling anomalies at NEC. Further results showed that the Arctic Oscillation (AO) acts as a bridge in the linkage between the autumn SICK and minimum temperature in NEC. Diminished autumn SICK leads to a weakened polar vortex in both the troposphere and the stratosphere in the subsequent winter through vertical propagation of planetary waves, contributing to a negative phase of the AO. Accordingly, the SICK is followed by anomalous Rossby wave train that originates from Mediterranean Sea and propagates eastward to Northeast Asia. Thus, the SICK has substantially influences on the Mongolia cyclone and minimum temperature in NEC by modulation of the AO. Moreover, the shrinking SICK could lead to the upper-level decelerated westerly anomalies at East Asia through altering the equator-to-pole temperature gradient and induce negative minimum temperature anomalies at NEC. The results of this study have importance regarding the prediction of low temperature anomalies over NEC.

Drought, a recurring natural phenomenon in South Asia's monsoon climate, presents challenges in delineating its spatiotemporal patterns within complex topographies. This study investigated the impact of the orographic barrier in the rice-dominated agricultural region of northeastern India and Bangladesh on drought characteristics during 1951–2020, employing the relative Standardized Precipitation Index (rSPI) and relative Standardized Precipitation-Evapotranspiration Index (rSPEI) across 3-, 6- and 12-month scales. The results indicate that even in the rainiest region of the world, droughts extend beyond the limits of the dry season inherent in the monsoon regime. These mostly regional droughts exhibit weak correlations with the core of the Indian subcontinent and other parts of Bangladesh. The region's orographic barrier has a greater influence on drought intensity than on frequency. The rSPI index, which depends solely on rainfall, may overestimate drought intensity and frequency in regions with high seasonal/annual rainfall and substantial intermonthly variability. In contrast, the rSPEI index, which depends on both rainfall and potential evapotranspiration (PET), better reflects the spatial variation of drought in complex terrain, identifying the leeward hinterland of the orographic barrier as the most drought-prone area. The two indices give similar results for drought characteristics away from the barrier. Furthermore, the orographic barrier exerts a negligible influence on the trends in rSPI and rSPEI. Principal component analysis (PCA) highlights the influences of the rainfall coefficient of variation and elevation on rSPI, while the PET coefficient of variation strongly influences rSPEI. Strategies to minimize the adverse effects of drought in complex topography and year-round cropping should be local and season-specific. These include using shorter-growing, drought-resistant rice varieties and adjusting planting schedules in rain shadow areas during the summer monsoon. These efforts should be complemented by integrating indigenous irrigation methods with modern practices such as roof water harvesting and tube wells in winter.