Subseasonal Scales Research Articles

Satellite Based Composite Weather Insurance Product for assessing mid-term adversary

The present study formulated a satellite-based rainfall insurance product (SRIP) at sub-seasonal scale during 2015–2020 by integrating meteorological, geophysical and vegetation index products e.g. rainfall (CHIRPS:Climate Hazards Group InfraRed Precipitation with Station), soil moisture (SMAP), normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) from moderate resolution imaging spectro-radiometer (MODIS). The SRIP was meticulously designed to integrate both cause (rainfall) and impact (NDVI, NDWI, soil moisture) parameters to enhance the assessment of min-season crop risks, with a particular emphasis on deficit rainfall. The performance evaluation of SRIP was conducted in two distinct agro-climatic zones viz. Deoria district of Uttar Pradesh (Upper Gangetic Plain Region) and Krishna district of Andhra Pradesh (Southern Plateau and Hills Region) spanned from June to October, which aligns with the major crop-growing season corresponding to the Southwest monsoon period in India. Three sets of SRIPs were investigated i.e. SRIP with cause parameter (SRIP1), SRIP with cause-impact parameters with no lag response (SRIP2) and SRIP with cause-impact parameters with one-month lag response (SRIP3). The SRIP was structured to provide a numerical index on a scale ranging from 0 to 1, representing poor to good crop-growing conditions that could effectively identify the rainfall deficit threshold (SRIP≤0.43) corresponding to negative percent deviation in rainfall (PDR≤−20%). This threshold level was recognized for compensation of triggers at the multiple crop growth phases during the crop-growing season. The district average of SRIP and PDR showed fair correlation co-efficient (0.77 ≤ r ≤ 0.88). Good correlation between sub-seasonal (monthly, bi-monthly, tri-monthly) SRIP and normalized MODIS Gross Primary Productivity (0.65 ≤ r ≤ 0.98), and SRIP deviation and yield deviation (0.45 ≤ r ≤ 0.97) reflect sensitivity of SRIP to crop carbon productivity and crop yield. The findings of the present study recommend SRIP not only a multi-phase composite index for assessing mid-season crop risks in terms of rainfall deficit for clusters of insurance units, but it has the potential to be used by insurance agencies to settle the claims by farmers in a transparent and faster manner for crop loss due to large-scale rainfall deficits. However, SRIP needs to be customized in future to make it more adaptable to settle insurance claims due to weather adversaries at individual farm level.

Precipitation variability at sub-seasonal scale under the ENSO influence in southeastern South America

Precipitation variability at sub-seasonal scale under the ENSO influence in southeastern South America

Interdecadal Changes in the Links Between Late‐Winter NAO and North Atlantic Tripole SST and Possible Mechanism

AbstractThe North Atlantic Oscillation (NAO) and North Atlantic tripole sea surface temperature (SST_tri) are important modes in the atmosphere and ocean over the North Atlantic, respectively. The link between the two is well‐known. However, this link weakened during 1980–2001, which is particularly pronounced in late winter and was not detected in early winter. This phenomenon has not been well revealed. The role of NAO in the above correlation changes was discussed. In late winter, a significant eastward shift (up to 20° longitude) of NAO south center during 1980–2001 was observed in both observation and CMIP6, accompanied by the eastward expansion of NAO north center. Spatial shift of the NAO forced the region of strong air‐sea interactions to shift and resulting in the collapse of NAO‐related SST_tri. These findings deepen our understanding of the NAO on the subseasonal scale.

Characteristics of the East Asian Summer Monsoon Using GK2A Satellite Data

In East Asia, where concentrated summer precipitation often leads to climate disasters, understanding the factors that cause such extreme rainfall is crucial for effective forecasting and preparedness. The western North Pacific subtropical high (WNPSH) is a key driver of summer precipitation variability, and therefore, its monitoring is critical to predicting the wet or dry periods during the East Asian summer monsoon. Using the Geo-KOMPSAT 2A (GK2A) satellite cloud amount data and ERA5 reanalysis data during the years 2020–2023, this study identified three leading empirical orthogonal function (EOF) modes and investigated the associated WNPSH variability at synoptic and subseasonal scales. The analysis includes a linear regression of meteorological fields onto the principal component (PC) time series. All three modes play a role in the spatiotemporal variability of the WNPSH, exhibiting lead–lag relationships. In particular, the second mode is responsible for its northwestward shift and intensification. As the WNPSH moves northwestward, the position of the monsoon rain band also shifts, and its intensity is modulated mainly by the moisture transport along the WNPSH boundary. Our results highlight the potential of high-resolution, real-time data from the GK2A satellite to elucidate WNPSH variability and its impact on the East Asian summer monsoon. By addressing the variability of the WNSPH using GK2A data, we pave the way for the development of a real-time monitoring framework with GK2A, which will improve our predictability and readiness for extreme weather events in East Asia.

Subtropical SST improved the understanding of the subseasonal reversal of surface air temperature in winter over the mid-high latitudes of Asia

The mid-high latitudes of Asia are cold air gathering places and dust source areas, whose subseasonal reversal of surface air temperature (SAT) between early winter (December 1 to January 14) and late winter (January 15 to February 28) could trigger extreme temperature at mid-low latitudes. Based on the observed interannual variation of SAT over mid-high latitudes of Asia (SATMH-A) in early and late winter after detrending from 1979 to 2021, the across-latitude linkage between the SATMH-A subseasonal reversal and subtropical sea surface temperature (SST) was revealed. The southern Indian Ocean SST anomalies caused the Ural high weakened through meridional circulations and led to a warm SATMH-A in early winter, but had little effect on SATMH-A in later winter. Whereas the North Atlantic SST anomalies enhanced the cold air intrusion in late winter by exciting Rossby wave to strengthen the Ural blocking, while the SATMH-A in early winter was largely unresponsive. The SST factors in Indian Ocean and North Atlantic primarily impacted the SATMH-A in early and late winter respectively. Their synergies effectively modulated the subseasonal reversal, which was conducive to improving the explanation and prediction for the SATMH-A subseasonal reversal. Especially in the winter of 2020/21 and 2021/22, the mean prediction bias was only 0.03 °C and 0.20 °C in early and late winter contributed by the two SST factors. Moreover, the improved prediction ability on the subseasonal scale was also helpful for the accurate predictions of SATMH-A on winter-mean scale. Findings of this study also have the potential to be applied to more accurately predict temperature extremes in mid-low latitudes.

Daily-Scale Prediction of Arctic Sea Ice Concentration Based on Recurrent Neural Network Models

Arctic sea ice prediction is of great practical significance in facilitating Arctic route planning, optimizing fisheries management, and advancing the field of sea ice dynamics research. While various deep learning models have been developed for sea ice prediction, they predominantly operate at the seasonal or sub-seasonal scale, often focusing on localized areas, and few cater to full-region daily-scale prediction. This study introduces the use of spatiotemporal sequence data prediction models, namely, the convolutional LSTM (ConvLSTM) and predictive recurrent neural network (PredRNN), for the prediction of sea ice concentration (SIC). Our analysis reveals that, when solely utilizing SIC historical data as the input, the ConvLSTM model outperforms the PredRNN model in SIC prediction. To enhance the models’ capacity to capture spatiotemporal relationships between multiple variables, we expanded the range of input data types to form the ConvLSTM-multi and PredRNN-multi models. Experimental findings demonstrate that the prediction accuracy of the four models significantly surpasses the CMIP6 model in three prospective climate scenarios (SSP126, SSP245, and SSP585). Of the four models, the ConvLSTM-multi model excels in assimilating the influence of reanalysis data on sea ice within the sea ice edge region, thus exhibiting superior performance than the PredRNN-multi model in predicting daily Arctic SIC over the subsequent 10 days. Furthermore, sensitivity tests on various model parameters highlight the substantial impact of sea surface temperature and prediction date on the accuracy of daily sea ice prediction, and meteorological and oceanographic parameters primarily affect the prediction accuracy of the thin-ice region at the edge of the sea ice.

Impact of climate change under the RCP8.5 emission scenario on multivariable agroclimatic indices in Western Canada from convection-permitting climate simulation

Climate change will impact crop production in Western Canada by modifying growing season conditions. Precipitation pattern changes and warmer temperatures will pose significant risks to crops. Studies have shown that multivariable agroclimatic indices can enhance climatic impact assessment on crop production. This study uses multivariable agroclimatic indices to assess how climate change may impact crop production in western Canada by the end of the 21st century. We use convection-permitting regional climate simulations for the current (CTL) and future climate under the Representative Concentration Pathway 8.5 scenario (RCP8.5) scenario based on the pseudo-global warming (PGW) approach to assess the impact of the climate on growing season indices. CTL and PGW are bias-corrected to the Global Environmental Multiscale (GEM) Canadian Precipitation Analysis (CaPA) (GEM-CaPA) dataset using the multivariate quantile mapping method. Our study analyses Effective Precipitation (Pe), Temperature Humidity Index (THI), and Precipitation Intensity Index (PII) at seasonal and sub-seasonal scales as they apply to cool-season crops. The CTL simulation shows a good performance in reproducing the spatial patterns and the temporal variability of the selected indices in western Canada. Results show that precipitation (Effective Precipitation) will decrease by over 60 mm (40 mm), rainy days will decrease by up to 10 days, and precipitation intensities will increase across western Canada. Warming will lead to THI unit increases of about 3.5 (>5) in the prairies (northeastern parts of western Canada in June). This study’s findings can be useful in generating appropriate information to inform policy on adaptation for sustainable crop production by the end of the 21st century.

Land Surface Modeling in the Himalayas: On the Importance of Evaporative Fluxes for the Water Balance of a High‐Elevation Catchment

AbstractHigh Mountain Asia (HMA) is among the most vulnerable water towers globally and yet future projections of water availability in and from its high‐mountain catchments remain uncertain, as their hydrologic response to ongoing environmental changes is complex. Mechanistic modeling approaches incorporating cryospheric, hydrological, and vegetation processes in high spatial, temporal, and physical detail have never been applied for high‐elevation catchments of HMA. We use a land surface model at high spatial and temporal resolution (100 m and hourly) to simulate the coupled dynamics of energy, water, and vegetation for the 350 km2 Langtang catchment (Nepal). We compare our model outputs for one hydrological year against a large set of observations to gain insight into the partitioning of the water balance at the subseasonal scale and across elevation bands. During the simulated hydrological year, we find that evapotranspiration is a key component of the total water balance, as it causes about the equivalent of 20% of all the available precipitation or 154% of the water production from glacier melt in the basin to return directly to the atmosphere. The depletion of the cryospheric water budget is dominated by snow melt, but at high elevations is primarily dictated by snow and ice sublimation. Snow sublimation is the dominant vapor flux (49%) at the catchment scale, accounting for the equivalent of 11% of snowfall, 17% of snowmelt, and 75% of ice melt, respectively. We conclude that simulations should consider sublimation and other evaporative fluxes explicitly, as otherwise water balance estimates can be ill‐quantified.

Exploring the Use of European Weather Regimes for Improving User-Relevant Hydrological Forecasts at the Subseasonal Scale in Switzerland

Abstract Across the globe, there has been an increasing interest in improving the predictability of subseasonal hydrometeorological forecasts, as they play a valuable role in medium- to long-term planning in many sectors, such as agriculture, navigation, hydropower, and emergency management. However, these forecasts still have very limited skill at the monthly time scale; hence, this study explores the possibilities for improving forecasts through different pre- and postprocessing techniques at the interface with a Precipitationn–Runoff–Evapotranspiration Hydrological Response Unit Model (PREVAH). Specifically, this research aims to assess the benefit of European weather regime (WR) data within a hybrid forecasting setup, a combination of a traditional hydrological model and a machine learning (ML) algorithm, to improve the performance of subseasonal hydrometeorological forecasts in Switzerland. The WR data contain information about the large-scale atmospheric circulation in the North Atlantic–European region, and thus allow the hydrological model to exploit potential flow-dependent predictability. Four hydrological variables are investigated: total runoff, baseflow, soil moisture, and snowmelt. The improvements in the forecasts achieved with the pre- and postprocessing techniques vary with catchments, lead times, and variables. Adding WR data has clear benefits, but these benefits are not consistent across the study area or among the variables. The usefulness of WR data is generally observed for longer lead times, e.g., beyond the third week. Furthermore, a multimodel approach is applied to determine the “best practice” for each catchment and improve forecast skill over the entire study area. This study highlights the potential and limitations of using WR information to improve subseasonal hydrometeorological forecasts in a hybrid forecasting system in an operational mode.

An increasing trend in daily monsoon precipitation extreme indices over Pakistan and its relationship with atmospheric circulations

This study assessed spatiotemporal trends in daily monsoon precipitation extremes at seasonal and sub-seasonal scales (June, July, August, and September) and their links with atmospheric circulations over Pakistan. The study used observed precipitation data from fifty in-situ stations and reanalysis products from the European Centre for Medium-Range Weather Forecasts (ECMWF) and National Centers for Environmental Prediction/the National Center for Atmospheric Research (NCEP/NCAR) during 1981–2018. A suite of seven extreme precipitation indices and non-parametric statistical techniques were used to infer trends in the frequency and intensity of extreme precipitation indices. An increase in frequency and intensity of overall extreme indices was evident, with a maximum tendency in the country’s northwestern (z-score=>2.5), central, and eastern (z-score > 4) monsoon-dominant parts. The northern and southwestern parts of the country exhibited a slight decrease (z-score <–2) in frequency and intensity. The Sen’s Slope estimator (SSE) shows an increase in western parts (0.20 days) indicating a shift in the maxima of the monsoon precipitation. The regional precipitation shows an increase in wet days (R1 mm) with higher values of mMK (3.71) and SSE (0.3) in region 2 Similar results of moderate regional increase are evident for extreme indices except regions 1 and 3. The extreme 1-day maximum precipitation increased in region 3 (mMK: 1.39, SSE: 2.32). The extremely wet days (R99p TOT) precipitation has a moderate increase in all regions with a decrease in region 1. The temporal mutations showed dynamic changes, clearly reflecting the country’s historical extreme events. The frequency and intensity of precipitation extremes negatively correlated with the altitude (R = −0.00039). The probability density function (PDF) showed a significant increase in the density during June and September with a probabilistic positive shift during July and August. The intensified mid-latitude westerlies and subtropical zonal easterlies teleconnections, strengthening of the monsoon trough, and land-ocean thermal contrast are the potential drivers of the increasing trend in precipitation extremes. The current study could serve as a benchmark for future researchers and policymakers to devise effective mitigation strategies for sustainable development.

Subseasonal Prediction of Regional Antarctic Sea Ice by a Deep Learning Model

AbstractAntarctic sea ice concentration (SIC) prediction at seasonal scale has been documented, but a gap remains at subseasonal scale (1–8 weeks) due to limited understanding of ice‐related physical mechanisms. To overcome this limitation, we developed a deep learning model named Sea Ice Prediction Network (SIPNet) that can predict SIC without the need to account for complex physical processes. Compared to mainstream dynamical models like European Centre for Medium‐Range Weather Forecasts, National Centers for Environmental Prediction, and Seamless System for Prediction and Earth System Research developed at Geophysical Fluid Dynamics Laboratory, as well as a relatively advanced statistical model like the linear Markov model, SIPNet outperforms them all, effectively filling the gap in subseasonal Antarctic SIC prediction capability. SIPNet results indicate that autumn SIC variability contributes the most to sea ice predictability, whereas spring contributes the least. In addition, the Weddell Sea displays the highest sea ice predictability, while predictability is low in the West Pacific. SIPNet can also capture the signal of ENSO and SAM on sea ice.

Hybrid Deep Learning and S2S Model for Improved Sub-Seasonal Surface and Root-Zone Soil Moisture Forecasting

Surface soil moisture (SSM) and root-zone soil moisture (RZSM) are key hydrological variables for the agricultural water cycle and vegetation growth. Accurate SSM and RZSM forecasting at sub-seasonal scales would be valuable for agricultural water management and preparations. Currently, weather model-based soil moisture predictions are subject to large uncertainties due to inaccurate initial conditions and empirical parameterization schemes, while the data-driven machine learning methods have limitations in modeling long-term temporal dependences of SSM and RZSM because of the lack of considerations in the soil water process. Thus, here, we innovatively integrate the model-based soil moisture predictions from a sub-seasonal-to-seasonal (S2S) model into a data-driven stacked deep learning model to construct a hybrid SSM and RZSM forecasting framework. The hybrid forecasting model is evaluated over the Yangtze River Basin and parts of Europe from 1- to 46-day lead times and is compared with four baseline methods, including the support vector regression (SVR), random forest (RF), convolutional long short-term memory (ConvLSTM) and the S2S model. The results indicate substantial skill improvements in the hybrid model relative to baseline models over the two study areas spatiotemporally, in terms of the correlation coefficient, unbiased root mean square error (ubRMSE) and RMSE. The hybrid forecasting model benefits from the long-lead predictive skill from S2S and retains the advantages of data-driven soil moisture memory modeling at short-lead scales, which account for the superiority of hybrid forecasting. Overall, the developed hybrid model is promising for improved sub-seasonal SSM and RZSM forecasting over global and local areas.

An integrated method for extended-range prediction of heavy Precipitation process in the flood season over Hunan Province based on S2S models

Abstract Floods in the middle reaches of Yangtze River threaten millions of people and cause casualties and economic losses. Yet, the prediction of floods especially on the sub-seasonal scale in this region is still challenging. To better predict the floods during the flood season (from April to August) in Hunan Province, the models from the China Meteorological Administration (CMA), the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) that participated in the sub-seasonal to seasonal (S2S) prediction project were chosen to evaluate their extended-range (the next 11–30 days) prediction skills for heavy precipitation. The original prediction score of single model (original score), the score of single model using optimal threshold of heavy precipitation (adjusted score) and the score of multi-model integration (integrated score) were calculated by the scoring rules for heavy precipitation process. The results show that the integrated score in the extended-range is 75.1, which is 10.3 and 6.9 higher than the average scores of original models and adjusted method, respectively. The false alarm (missing) rate of the integrated method is 5.4% (33.9%), which is 8.6% (4.7%) and 2.9% (3.3%) smaller than the average rates of original models and adjusted method, respectively.

Subseasonal transition of Barents–Kara sea-ice anomalies in winter related to the reversed warm Arctic–cold Eurasia pattern

Subseasonal reversal of the warm Arctic–cold Eurasia pattern (WACE) could trigger an extreme cold/warm transition in winter and sandstorms in spring over eastern China. An associated subseasonal transition of the sea-ice anomaly also occurs in the Barents–Kara seas (BKS) driven by such remarkable high-latitude atmospheric pattern reversals. Under a warm Arctic and enhanced Ural high, abnormal downward turbulent heat flux and increased downward infrared radiation in the BKS are conducive to sea ice melting. The surface southerly wind drives the sea ice to drift from the thin to perennial ice area and further enlarges the open ocean surface. The opposite mechanism occurs in the opposite phase of WACE, causing positive BKS sea-ice anomalies. When WACE reverses on the subseasonal scale, the above mechanisms occur in early and late winter, respectively, resulting in a significant subseasonal transition of BKS sea-ice anomalies. More importantly, in the last decade, with a more frequent reversal of WACE, the subseasonal transition between early winter and late winter in BKS sea ice has enhanced. The findings of this study establish a comprehensive schematic of the subseasonal reversal of WACE and contribute to better understanding and predicting extreme climate in eastern China.

The Philippine springtime (February–April) sub-seasonal rainfall extremes and extended-range forecast skill assessment using the S2S database

During the first half month of April 2022, the Philippines experienced severe disasters associated with the weak but deadly tropical storm Megi that caused 214 deaths and two sunken ships. This prompted us to investigate the extended-range prediction skill of the springtime Philippine sub-seasonal scale rainfall extremes in the subseasonal-to-seasonal (S2S) prediction database. The results suggest that the S2S models can well predict the extremity of the 2022 springtime sub-seasonal peak rainfall event (SPRE) ten days ahead. In addition to the La Niña sea surface temperature anomalies, this prolonged rainfall event, from March 26 - April 14, 2022, was associated with an anomalous cyclonic circulation straddled over the South China Sea (SCS) and the Philippine Sea and persisted for two weeks. The strong relationship between the El Niño and Southern Oscillation (ENSO) and the springtime (February–April) rainfall variability in the Philippines is clearly revealed in the analysis of 25 years of observational and hindcast data. The extremely wet SPREs tend to occur during the La Niña springs, while the extremely dry SPREs tend to occur during the El Niño springs. The Madden-Julian oscillation (MJO) and equatorial Rossby (ER) waves that are capable of modulating the sub-seasonal rainfall extremes were weak when the deadly SPRE occurred in April 2022. Thus, the extended-range forecast skill of this example can be interpreted as the baseline skill of the current S2S prediction revealed in the multi-model database. The findings suggest that the SPRE is a useful item to be included in the operational forecast as potential opportunities to harness the benefits of S2S prediction and applications.

Object-based evaluation of rainfall forecasts over eastern China by eight cumulus parameterization schemes in the WRF model

Traditional pointwise verification is not fully accurate for the evaluation of rainfall forecasts due to the double-penalty problem. An alternative object-based metric, Method for Object-based Diagnostic Evaluation (MODE), was used in this study to evaluate the rainfall simulation performance of eight cumulus parameterization (CP) schemes in the Weather Research and Forecasting model. The average rainfall from May to August (MJJA) over eastern China from 2009 to 2018 and the sub-seasonal evolution of rainfall systems were mainly verified. The results showed that the heavy rainfall objects simulated by KFCuP, New Tiedtke and KSAS schemes are the closest to the observations over South China, Yangtze River Basin and North China, respectively. MODE could highlight the advantages of these schemes in the simulation of rainband physical attributes, such as centroid position, orientation, rainfall area, etc. At the sub-seasonal scale, KFCuP had the highest skill score for mimicking the observed heavy rainfall objects. To explore the reasons for the differences in rainfall simulations, the evaporation, water vapour inflow and precipitation efficiency were calculated for each scheme. It was found that members with higher precipitation generally corresponded to larger water vapour inflow and precipitation efficiency, and the water vapour inflow mainly came from the southern boundary. Further analysis found that there existed a significant positive correlation between the meridional wind and the water vapour inflow on southern boundary, which indicated that the meridional wind was an important factor affecting the regional rainfall simulation.

The intensification of coastal hypoxia off central Chile: Long term and high frequency variability

Hypoxia is a phenomenon where dissolved oxygen (DO) is reduced to levels that are low enough to strongly affect ecological and biogeochemical processes. This occurs within the continental shelf off central Chile (36°S), influenced by seasonal coastal upwelling (Spring-Summer). Monthly measurements of DO and other oceanographic variables in the water column (1997−2021) over the 92 m isobath along with high-resolution and near-surface observations (POSAR buoy), are analyzed to examine incidences of hypoxia and understand the physical and biogeochemical processes modulating DO vertical distribution and its temporal variability. On average, the percentage of the water column with DO levels below 89 (hypoxia) and 22 (severe hypoxia) μmol L−1 reaches 68% (i.e., hypoxic waters are found below 30 m) and 44% (below 50 m depth), respectively, but during the upwelling season, as much as 87% (below 12 m depth) and 81% (below 17 m depth) of the water column exhibits these levels. On the sub-seasonal scale during upwelling season six hypoxic events lasting at least 2 days occur at 10 m depth. There is a strong seasonal correlation between the volume of the seawater presenting hypoxia and upwelling favorable winds. Furthermore, there is a high DO interannual variability partially related to the El Niño Southern Oscillation (ENSO). Over 2 decades, it is estimated that DO concentration in surface and subsurface layers decreases (up to 21 μmol L−1 decade−1) as waters get colder (up to 0.29°C decade−1). Remarkably, the volume of hypoxic and severe hypoxic waters over the shelf has increased more than 2 times since 1997 and shows a significant positive correlation with the upwelling index. These preliminary findings indicate that the increase in local DO consumption is partially associated with upwelling intensification. Given the clear evidence of wind intensification in coastal upwelling ecosystems and thus the increase in hypoxic events, the coastal zone may be highly vulnerable to hypoxia, impacting biological resources and biogeochemical cycles.

Predicting the Daily Sea Ice Concentration on a Subseasonal Scale of the Pan-Arctic During the Melting Season by a Deep Learning Model

Predicting the Daily Sea Ice Concentration on a Subseasonal Scale of the Pan-Arctic During the Melting Season by a Deep Learning Model

Using a Synoptic-Scale Mixing Diagnostic to Explain Global Precipitation Variability from Weekly to Interannual Time Scales

Abstract Precipitation often happens along organized filaments or bands of moisture such as convergence zones. Recent regional studies have shown that these moisture filaments arise from synoptic-scale mixing features known as attracting Lagrangian coherent structures (LCSs). In this study, we present a climatology of synoptic-scale mixing and investigate its covariability with precipitation on temporal scales ranging from weekly to interannual. We characterize mixing with the finite-time Lyapunov exponent (FTLE), a measure of parcel deformation, in ERA5 data between 1980 and 2009. Attracting LCSs are identified as ridges of the FTLE. At the interannual time scale, we compare El Niño and La Niña events and find that composites of precipitation and mixing anomalies share similar spatial patterns. We also compare summer and winter seasons and find that composites of seasonal-mean precipitation and mixing anomalies present similar characteristics, with precipitation being particularly intense (weak) where mixing is strong (weak). In particular, these patterns closely match the typical signatures of the intertropical convergence zone (ITCZ) and monsoon systems and the migrations of extratropical cyclone tracks. At the subseasonal scale, we employ daily composites to investigate the influence of the Madden–Julian oscillation and the North Atlantic Oscillation on the mixing regimes of the Atlantic and east Pacific; our results indicate that these oscillations control the synoptic-scale horizontal mixing and the occurrence of LCSs as to suppress or enhance precipitating systems like the ITCZ and the South Atlantic convergence zone. The results presented in this first climatology of synoptic-scale mixing and LCSs indicate that these are powerful diagnostics to identify circulation mechanisms underlying precipitation variability.

A sub-seasonal oscillation of sea surface temperature in the Southern Indian Ocean during DJF and its excitation mechanism

Southern Indian Ocean dipole (SIOD) is a dipole SST anomaly in the Southern Indian Ocean that plays an important role in tropical climate variability. However, previous studies focused on the interannual and interdecadal scales. In this paper, the characteristics on sub-seasonal scale of SIOD are revealed from December to February (DJF) based on the ERA-Interim reanalysis dataset, and the excitation mechanisms of SIOD are also discussed. The results show that the two dominant modes of SST in the Southern Indian Ocean are the spatial distribution of Southwest-Northeast direction dipole (SIOD) and triple (SIOT) respectively, which has obvious period of quasi 50–60 days. Moreover, the intensity and zonal oscillation of the Mascarene high are conducive to the formation of SIOD (SIOT). The air–sea interaction during the formation process is composed of three stages. In the first stage the atmospheric forces the ocean to result in the Mascarene high westward (eastward) and enhances the abnormal anticyclone in the Southern Indian Ocean. There are abnormal northerly (southerly) flows on the west (east) sides of the abnormal anticyclone respectively, which weakens (enhances) the southeast trade wind in the climatology. The surface latent heat flux release decreases (increases) and SST is warmed (cooled). During the following stage, the ocean feedback the atmosphere. The warm SST continues to increase, resulting in low-level convective enhancement, which weakens the abnormal anticyclone. The third stage is again the atmosphere forcing the ocean. The abnormal anticyclone gradually turns into an abnormal cyclone and the meridional wind direction is reversed. The release of the latent heat flux increases (decreases) significantly which leads to the cooling (warming) of SST on the west (east) sides of an abnormal cyclone. In addition, the formation and extinction of SIOT are easier affected by the southern annular mode (SAM) than SIOD. The abnormal zonal wave train with wavenumber 4 (3) propagates the Southern Indian Ocean by the westerly jet waveguide and results in an SST anomaly of SIOD (SIOT), accompanied by an obvious sub-seasonal meridional variation of the precipitation in Southern Africa.