Seasonal Cycle Of Rainfall Research Articles

Using the Observed Variations of the Start Date of the Rainy Season over Central America for Its Reliable Seasonal Outlook

Abstract We introduce a simple method to define the start and the end of the rainiest part of the year as the first and the last day of the year when the daily rain rate is more or less than the annual mean climatological rain rate for a region or at a given grid point of the rainfall analysis, respectively. A novelty of this work is the adoption of a perturbation technique to generate a total of 1001 ensemble members to account for observational and analysis uncertainties. This allows for a probabilistic estimate of the start and retreat dates of the rainy season at the granularity of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), version 6, rainfall analysis over Central America. The seasonal cycle of the IMERG rainfall analysis is also found to verify with in situ observations in the region. Many large-scale climate drivers affect regional rainfall, often with complex interactions that affect the onset date, retreat date, and magnitude of the seasonal rainfall cycle, making it difficult to predict the length or total quantity of seasonal rainfall using climate drivers alone. Once an onset date is established, however, this metric alone can be more indicative of both the length and total seasonal rainfall anomaly than predicting how the climate drivers will interact to affect the quantity and duration of upcoming seasonal rainfall. The local relationships of the start date with seasonal length and rainfall anomaly are leveraged to produce effective seasonal outlooks of the rainy season for the region by just monitoring the start date variations. Significance Statement The start date and retreat date of the rainy season in Central America are defined every year, precisely to a specific date from our proposed definition of it. This is possible because the region exhibits a strong seasonality of the rainfall. As a result, the year-to-year (interannual) variation of the seasonal rainfall during the rainy season is also determined by the variations in the length of the season that are usually overlooked in fixed calendar seasons. We define the start or retreat date of the rainy season as the first or the last day of the year when the daily rain rate exceeds or falls below the average daily rainfall from 2001 to 2022, respectively. We also find that both start and retreat date variations independently influence the length and total seasonal rainfall of the rainy season. Consequently, these relationships are leveraged to provide an outlook of the forthcoming rainy season from the diagnosis of the variations of its onset date, which is shown to be an effective predictor with significant useful seasonal prediction skills.

Australian Summer Monsoon: Reanalyses Versus Climate Models in Moist Static Energy Budget Evolution

AbstractThe Australian summer monsoon (ASM) influences the tropical hydro‐climate of Northern Australia during the extended summer months (October–April). Despite advances in understanding the ASM, climate models vary widely in their depiction and projections of its future behavior remain uncertain. This study investigates the moist static energy (MSE) budget and examines the gross moist stability (GMS) evolution throughout the monsoon cycle using two reanalysis data sets. We then assess the ability of Atmospheric Modeling Intercomparison Project (AMIP) simulations of climate models to reproduce not only the monsoon seasonal cycle of rainfall but the associated mechanisms revealed by the budget analysis. The budget analysis shows a strong influence of the regions to the north and west of our study area for the import of moisture and export of energy into and away from the ASM. We find that models reproduce this influence qualitatively, but not quantitatively. As in previous studies, we identify two major regimes of the GMS associated with the absence (higher GMS) or presence (lower GMS) of convection. Whilst climate models are able to distinguish the two regimes, they significantly overestimate the GMS in convectively active periods, owing largely to profile of ascent that is too top heavy. Models with more realistic precipitation do not consistently offer more accurate representations of dynamic processes, as evaluated by the MSE budget and GMS. This highlights limitations in assessing models based solely on single variables. To enhance the generalizability of these findings, future studies should employ models without prescribed sea surface temperatures.

Revisiting the Seasonal Cycle of Rainfall over Central Africa

Abstract The intertropical convergence zone (ITCZ), with its twice-annual passage over central Africa, is considered as the main driver of the rainfall seasonality. In this ITCZ paradigm, high rainfall occurs over regions of large low-level convergence. But recently, this paradigm was challenged over central Africa. Here, we show that a shallow meridional overturning circulation—driven by surface conditions—plays a thermodynamical control on the rainfall seasonality over central Africa. Indeed, due to the local evaporative cooling effect, the foot of the ascending branch of Hadley cells occurs where the temperature is the warmest, indicating a thermal low. This distorts the southern Hadley cell by developing its bottom-heavy structure. As result, both shallow and deep Hadley cells coexist over central Africa year-round. The deep mode is associated with the poleward transport of atmospheric energy at upper levels. The shallow mode is characterized by a shallow meridional circulation, with its moisture transport vanishing and converging in the midtroposphere rather than at lower troposphere. This midtropospheric moisture convergence is also the dominant component that shapes the vertically integrated moisture flux convergence, with little contribution of African easterly jets. This convergence zone thus controls the precipitating convection. Its meridional migration highlights the interhemispheric rainfall contrast over central Africa and outlines the unimodal seasonality. On the other hand, forced by the Congo basin cell, the precipitable water regulates the deep convection from the vegetated surface of Congo basin, acting as a continental sea. This nonlinear mechanism separates the rainfall into three distinct regimes: the moisture-convergence-controlled regime, with convective rainfall exclusively occurring in the rainy season; the local evaporation-controlled regime with drizzle in the dry season; and the precipitable-water-controlled regime, with exponential rainfall increase in the dry season.

Ground-based climate data show evidence of warming and intensification of the seasonal rainfall cycle during the 1960–2020 period in Yangambi, central Congo Basin

Meteorological stations are rare in central Africa, which leads to uncertainty in regional climatic trends. This is particularly problematic for the Congo Basin, where station coverage decreased significantly during the last few decades. Here, we present a digitized dataset of daily temperature and precipitation from the Yangambi biosphere reserve, covering the period 1960–2020 (61 years) and located in the heart of the Congo Basin. Our results confirm a long-term increase in temperature and temperature extremes since the 1960s, with strong upward trends since the early 1990s. Our results also indicate a drying trend for the dry season and intensification of the wet season since the early 2000s. Ongoing warming and increasing precipitation seasonality and intensity already have a significant impact on crop yields in Yangambi. This calls for urgent development of climate-smart and dynamic agriculture and agroforestry systems. We conclude that systematic digitization and climate recording in the Congo Basin will be critical to improve much-needed gridded benchmark datasets of climatic variables.

The seasonal predictability of the wet season over Peninsular Florida

AbstractIn this study, we examine the seasonal predictability of Peninsular Florida (PF)'s boreal summer season, which is also known as the PF wet season (PFWS) due to the coinciding peak of the robust seasonal cycle of rainfall. The seasonal predictability is examined in the Community Climate System Model, version 4 (CCSM4), which is one of the models of the North American Multi‐Model Ensemble used for routine, operational seasonal forecasts by the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC). An objective definition of the onset and the demise of the PFWS are then implemented, and the predictability of the onset, the demise and the seasonal rainfall anomaly is assessed from the CCSM4 seasonal hindcasts. Our study shows that the 33 years (1983–2015) of the CCSM4 seasonal hindcasts display very poor deterministic and probabilistic skill in predicting the onset and the demise of the PFWS and its seasonal rainfall anomalies for all lead times. It is shown that the seasonal hindcasts display poor skill for both the fixed calendar month and varying seasonal length of the PFWS. In many of these instances, the skills deteriorate with the lead time of the CCSM4 hindcasts, although, in some cases, they bear no relation (e.g., demise date of the PFWS). Our analysis reveals that the interannual variability of the location of the western ridge of the North Atlantic subtropical high is poorly rendered in the seasonal hindcast of CCSM4 with respect to reanalysis. This could explain the origins of some of the potential issues of the CCSM4 seasonal hindcasts with the variability of the PFWS.

Using seasonal rainfall clusters to explain the interannual variability of the rain belt over the Greater Horn of Africa

AbstractThe seasonal cycle of rainfall over the Greater Horn of Africa (GHA) is dominated by the latitudinal migration and activity of the tropical rain belt (TRB). The TRB exhibits high interannual variability in the GHA and the reasons for the recent dry period in the Long Rains (March–May) are poorly understood. In addition, few studies have addressed the rainfall fluctuations during the Msimu Rains (Dec.–Mar.) in the southern GHA region. Interannual variations of the seasonal cycle of the TRB between 1981 and 2018 were analysed using two statistical indices. The Rainfall Cluster Index (RCI) describes the seasonal cycle as a succession of six characteristic rainfall patterns, while the Seasonal Location Index (SLI) captures the latitudinal location of the TRB. The SLI and RCI depict the full seasonal cycle of the TRB supporting interpretations of the interannual variations and trends. The Msimu Rains are dominated by two clusters with opposite rainfall characteristics between the Congo Basin and Tanzania. The associated anomalies in moisture flux and divergence indicate variations in the location of the TRB originating from an interplay between low‐level air flows from the Atlantic and Indian Oceans and tropical and subtropical teleconnections. The peak period of the Long Rains shows a complex composition of five clusters, which is tightly connected to intraseasonal and interannual variability of latitudinal locations of the TRB. A persistent location of the TRB near the equator, evidenced in a frequent occurrence of a cluster related to an anomalously weak Walker circulation, is associated with wet conditions over East Africa. Dry Long Rains are associated with strong and frequent latitudinal variations of the TRB position with a late onset and intermittent rainfall. These results offer new opportunities to understand recent variability and trends in the GHA region.

Impact of climate change on streamflow regime of a large Indian river basin using a novel monthly hybrid bias correction technique and a conceptual modeling framework

The study analyses the impact of climate change on streamflow regime of the Mahanadi River basin (MRB) in projected climate scenarios obtained from CMIP5 models. Projections from a total of 9 Global Climate Models (GCMs) were utilized. Integrated MIKE 11 NAM-HD, a conceptual hydrological model was employed to simulate the streamflow at Hirakud and Mundali gauging sites of upper Mahanadi River basin (UMRB) and middle Mahanadi River basin (MMRB), respectively. Prior to generating the streamflow regimes for the projected scenarios, GCM simulated precipitation and temperature were bias corrected using India Meteorological Department (IMD) observed gridded precipitation and temperature datasets. Precipitation was corrected using quantile mapping technique employing four different approaches, i.e., seasonal, hybrid, monthly non-hybrid, and monthly hybrid approaches. Hybrid approaches explicitly correct the extreme rainfall using Generalized Extreme Value (GEV) distribution whereas Gamma distribution is used for normal rainfall. Temperature data was corrected using Gaussian distribution on daily basis employing monthwise time series. L-moments based frequency analysis method was used to estimate the 50-year return period rainfall for observed and bias corrected projected time series to assess the efficacy of bias correction methods in correcting the extreme events. Monthly hybrid approach was found to resolve rainfall climatology over both UMRB and MMRB with improved skill in extreme rainfall correction. Though, monthly non-hybrid approach produced the seasonal cycle of rainfall with highest accuracy, it performed poorly in correcting the extremes. Under projected climate scenarios mean annual rainfall is found to be increasing for BCC-CSM1.1(m), HadGEM2-AO, GFDL-CM3, and IPSL-CM5A-LR whereas other GCMs show mixed signal compared to their mean state of historic period (1976–2005). Streamflow regime in projected climate was analyzed using ensemble mean of simulated streamflow from identified GCMs. Mean monthly streamflow were overall found to be increasing towards the end of the century i.e., in far-future defined as 2070–2099. Daily high flows (defined at 5% and 10% exceedance probability) were found to be increasing in their magnitude and frequency in far-future whereas occurrences of low flows (defined at 90% and 95% exceedance probability) were found to be predominantly decreasing under projected climate.

The American monsoon system in HadGEM3 and UKESM1

Abstract. The simulated climate of the American monsoon system (AMS) in the UK models HadGEM3 GC3.1 (GC3) and the Earth system model UKESM1 is assessed and compared to observations and reanalysis. We evaluate the pre-industrial control, AMIP and historical experiments of UKESM1 and two configurations of GC3: a low (1.875∘×1.25∘) and a medium (0.83∘×0.56∘) resolution. The simulations show a good representation of the seasonal cycle of temperature in monsoon regions, although the historical experiments overestimate the observed summer temperature in the Amazon, Mexico and Central America by more than 1.5 K. The seasonal cycle of rainfall and general characteristics of the North American monsoon of all the simulations agree well with observations and reanalysis, showing a notable improvement from previous versions of the HadGEM model. The models reasonably simulate the bimodal regime of precipitation in southern Mexico, Central America and the Caribbean known as the midsummer drought, although with a stronger-than-observed difference between the two peaks of precipitation and the dry period. Austral summer biases in the modelled Atlantic Intertropical Convergence Zone (ITCZ), cloud cover and regional temperature patterns are significant and influence the simulated regional rainfall in the South American monsoon. These biases lead to an overestimation of precipitation in southeastern Brazil and an underestimation of precipitation in the Amazon. The precipitation biases over the Amazon and southeastern Brazil are greatly reduced in the AMIP simulations, highlighting that the Atlantic sea surface temperatures are key for representing precipitation in the South American monsoon. El Niño–Southern Oscillation (ENSO) teleconnections, of precipitation and temperature, to the AMS are reasonably simulated by all the experiments. The precipitation responses to the positive and negative phase of ENSO in subtropical America are linear in both pre-industrial and historical experiments. Overall, the biases in UKESM1 and the low-resolution configuration of GC3 are very similar for precipitation, ITCZ and Walker circulation; i.e. the inclusion of Earth system processes appears to make no significant difference for the representation of the AMS rainfall. In contrast, the medium-resolution HadGEM3 N216 simulation outperforms the low-resolution simulations due to improved SSTs and circulation.

Interannual variability of the early and late-rainy seasons in the Caribbean

The Caribbean seasonal rainfall cycle and its characteristics are heavily relied upon by the region’s inhabitants for their socioeconomic needs; the prediction of its variability would be valuable to society. An important way to understand the predictability of the Caribbean rainfall cycle is to study its interannual variability. Previous studies vary as to how and what large-scale climate driver(s) affect the interannual variability of rainfall and its associated dynamical mechanisms in the Caribbean. To address this, this study investigates wet and dry Caribbean early-rainy seasons (ERS; mid-April to mid-June) and late-rainy seasons (LRS; late August to mid-November) by conducting the following: (1) a spatial composite of rainfall from 34 Caribbean rainfall stations using daily data; and, (2) spatial composites of sea-surface temperature, sea-level pressure, and mean flow moisture convergence and transports. The ERS and LRS are impacted in distinctly different ways by two different, and largely independent, dominant large-scale phenomena: the North Atlantic Oscillation (NAO) and the El Niño-Southern Oscillation (ENSO), respectively. Dry ERS years are associated with a persistent dipole of cold and warm SSTs over the Caribbean Sea and Gulf of Mexico, respectively, that were caused by a preceding positive NAO state. This setting involves a wind-evaporation-SST (WES) feedback expressed in enhanced trade winds and consequently, moisture transport divergence over all of the Caribbean, except in portions of the NW Caribbean in May. A contribution from the preceding winter cold ENSO event is also discernible during dry ERS years. Dry LRS years are due to the summertime onset of an El Niño event, developing an inter-basin SLP pattern that fluxes moisture out of the Caribbean, except in portions of the NW Caribbean in November. Both large-scale climate drivers would have the opposite effect during their opposite phases leading to wet years for both seasons. The two rainy seasons are independent because the main drivers of their variability are independent. This has implications for prediction.

The end of the African humid period as seen by a transient comprehensive Earth system model simulation of the last 8000 years

Abstract. Enhanced summer insolation during the early and mid-Holocene drove increased precipitation and widespread expansion of vegetation across the Sahara during the African humid period (AHP). While changes in atmospheric dynamics during this time have been a major focus of palaeoclimate modelling efforts, the transient nature of the shift back to the modern desert state at the end of this period is less well understood. Reconstructions reveal a spatially and temporally complex end of the AHP, with an earlier end in the north than in the south and in the east than in the west. Some records suggest a rather abrupt end, whereas others indicate a gradual decline in moisture availability. Here we investigate the end of the AHP based on a transient simulation of the last 7850 years with the comprehensive Earth system model MPI-ESM1.2. The model largely reproduces the time-transgressive end of the AHP evident in proxy data, and it indicates that it is due to the regionally varying dynamical controls on precipitation. The impact of the main rain-bringing systems, i.e. the summer monsoon and extratropical troughs, varies spatially, leading to heterogeneous seasonal rainfall cycles that impose regionally different responses to the Holocene insolation decrease. An increase in extratropical troughs that interact with the tropical mean flow and transport moisture to the western Sahara during the mid-Holocene delays the end of the AHP in that region. Along the coast, this interaction maintains humid conditions for a longer time than further inland. Drying in this area occurs when this interaction becomes too weak to sustain precipitation. In the lower latitudes of west Africa, where the rainfall is only influenced by the summer monsoon dynamics, the end of the AHP coincides with the retreat of the monsoonal rain belt. The model results clearly demonstrate that non-monsoonal dynamics can also play an important role in forming the precipitation signal and should therefore not be neglected in analyses of north African rainfall trends.

Recent intensification of the seasonal rainfall cycle in equatorial Africa revealed by farmer perceptions, satellite-based estimates, and ground-based station measurements

Smallholder farmers and livestock keepers in sub-Saharan Africa are on the frontlines of climate variability and change. Yet, in many regions, a paucity of weather and climate data has prevented rigorous assessment of recent climate trends and their causes, thereby limiting the effectiveness of forecasts and other services for climate adaptation. In rainfed systems, farmer perceptions of changing rainfall and weather patterns are important precursors for annual cropping decisions. Here, we propose that combining such farmer perceptions of trends in seasonal rainfall with satellite-based rainfall estimates and climate station data can reduce uncertainties regarding regional climatic trends. In western Uganda, a rural and climatically complex transition zone between eastern and central equatorial Africa, data from 980 smallholder households suggest distinct changes in seasonal bimodal rainfall over recent decades, specifically wetter rainy seasons and drier dry seasons. Data from three satellite-based rainfall products beginning in 1983 largely corroborate respondent perceptions over the last 10–20 years, particularly in the southernmost sites near Queen Elizabeth National Park. In addition, combining all three information sources suggests an increasing trend in annual rainfall, most prominently in the north near Murchison Falls National Park over the past two decades; this runs counter to recent research asserting the presence of a drying trend in the region. Our study is unique in evaluating and cross-validating these multiple data sources to identify climatic change affecting people in a poorly understood region, while providing insights into regional-scale climate controls.

Climate model projections for future seasonal rainfall cycle statistics in Northwest Costa Rica

Increasing water scarcity due to rising demand and changes in climate and land use are expected to exert significant stress on water resources in many parts of the world. In many areas, distinctive patterns of seasonal precipitation play an important role in regional ecosystems, economies, and food and energy supplies. Given the inconsistency among climate model precipitation projections, this study develops a statistical approach to better characterize seasonal patterns of precipitation and to assess the potential impact of climate change on these patterns. The method is applied to evaluate the bimodal seasonal pattern of precipitation in northwest Costa Rica as a case study. A Gaussian mixture model is used to describe the bimodal pattern and quantify changes in the seasonal precipitation cycle projected by 19 Coupled General Circulation Models. The model simulations for the current period (1979–2005) are compared with observed monthly precipitation data based on four goodness‐of‐fit metrics to select the best performing models. The monthly bias‐corrected and spatially disaggregated (BCSD) climate projections from the selected models are used to investigate the projected change in the bimodal seasonal pattern, seasonal mean precipitation and interannual variability at the late twenty‐first century. Under RCP8.5, the degree of consistency associated with these BCSD climate projections is found to be higher for the projections of the midsummer drought than that for the early portion of the wet season (early season). For the late portion of the wet season (late season) projections, the degree of consistency is found to be the lowest. The proposed method provides (a) an overall characterization of inconsistencies in climate model projections for the region, (b) more information about the possible changes in the seasonal cycle of precipitation, and (c) the possibility of performing detailed comparison tests among climate models over a set of simulations and projections.

Seasonal climatology and dynamical mechanisms of rainfall in the Caribbean

The Caribbean is a complex region that heavily relies on its rainfall cycle for its economic and societal needs. This makes the Caribbean especially susceptible to hydro-meteorological disasters (i.e. droughts and floods). Previous studies have investigated the seasonal cycle of rainfall in the Caribbean with monthly or longer resolutions that often mask the seasonal transitions and regional differences of rainfall. This has resulted in inconsistent findings on the seasonal cycle. In addition, the mechanisms that shape the climatological rainfall cycle in the region are not fully understood. To address these problems, this study conducts: (i) a principal component analysis of the annual cycle of precipitation across 38 Caribbean stations using daily observed precipitation data; and, (ii) a moisture budget analysis for the Caribbean, using the ERA-Interim Reanalysis. This study finds that the seasonal cycle of rainfall in the Caribbean hinges on three main facilitators of moisture convergence: the Eastern Pacific ITCZ, the Atlantic ITCZ, and the western flank of the North Atlantic Subtropical High (NASH). The Atlantic Warm Pool and Caribbean Low-Level Jet modify the extent of moisture provided by these main facilitators. The expansion and contraction of the western flank of NASH generate the bimodal pattern of the precipitation annual cycle in the northwestern Caribbean, central Caribbean, and with the Eastern Pacific ITCZ the western Caribbean. This study identifies the Atlantic ITCZ as the major source of precipitation for the central and southern Lesser Antilles, which is responsible for their unimodal rainfall pattern. Convergence by sub-monthly transients contributes little to Caribbean rainfall.

How Accurately Contemporary Models Can Predict Monsoons?

Seasonal changes exhibit climate changes, so models can predict future climate change accurately only if they can reproduce seasonal cycle accu-rately. Further, seasonal changes are much larger than the changes even in long period of centuries. Thus it is unwise to ignore large ones compared to small climate change. In this paper, we determine how accurately a suite of ten coupled general circulation models reproduce the observed seasonal cycle in rainfall of the tropics. The seasonal cycles in rainfall of global tropics are known as monsoons. We found that the models can reasonably reproduce the seasonal cycle in rainfall, thus are useful in climate prediction and simulation of global monsoons.

Rainforest-initiated wet season onset over the southern Amazon

Although it is well established that transpiration contributes much of the water for rainfall over Amazonia, it remains unclear whether transpiration helps to drive or merely responds to the seasonal cycle of rainfall. Here, we use multiple independent satellite datasets to show that rainforest transpiration enables an increase of shallow convection that moistens and destabilizes the atmosphere during the initial stages of the dry-to-wet season transition. This shallow convection moisture pump (SCMP) preconditions the atmosphere at the regional scale for a rapid increase in rain-bearing deep convection, which in turn drives moisture convergence and wet season onset 2-3 mo before the arrival of the Intertropical Convergence Zone (ITCZ). Aerosols produced by late dry season biomass burning may alter the efficiency of the SCMP. Our results highlight the mechanisms by which interactions among land surface processes, atmospheric convection, and biomass burning may alter the timing of wet season onset and provide a mechanistic framework for understanding how deforestation extends the dry season and enhances regional vulnerability to drought.

Tree–grass phenology information improves light use efficiency modelling of gross primary productivity for an Australian tropical savanna

Abstract. The coexistence of trees and grasses in savanna ecosystems results in marked phenological dynamics that vary spatially and temporally with climate. Australian savannas comprise a complex variety of life forms and phenologies, from evergreen trees to annual/perennial grasses, producing a boom–bust seasonal pattern of productivity that follows the wet–dry seasonal rainfall cycle. As the climate changes into the 21st century, modification to rainfall and temperature regimes in savannas is highly likely. There is a need to link phenology cycles of different species with productivity to understand how the tree–grass relationship may shift in response to climate change. This study investigated the relationship between productivity and phenology for trees and grasses in an Australian tropical savanna. Productivity, estimated from overstory (tree) and understory (grass) eddy covariance flux tower estimates of gross primary productivity (GPP), was compared against 2 years of repeat time-lapse digital photography (phenocams). We explored the phenology–productivity relationship at the ecosystem scale using Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices and flux tower GPP. These data were obtained from the Howard Springs OzFlux/Fluxnet site (AU-How) in northern Australia. Two greenness indices were calculated from the phenocam images: the green chromatic coordinate (GCC) and excess green index (ExG). These indices captured the temporal dynamics of the understory (grass) and overstory (trees) phenology and were correlated well with tower GPP for understory (r2 = 0.65 to 0.72) but less so for the overstory (r2 = 0.14 to 0.23). The MODIS enhanced vegetation index (EVI) correlated well with GPP at the ecosystem scale (r2 = 0.70). Lastly, we used GCC and EVI to parameterise a light use efficiency (LUE) model and found it to improve the estimates of GPP for the overstory, understory and ecosystem. We conclude that phenology is an important parameter to consider in estimating GPP from LUE models in savannas and that phenocams can provide important insights into the phenological variability of trees and grasses.

Rainfall Trends, Drought Frequency and La Niña in Tuvalu: A Small Equatorial Island State in the Pacific Ocean

Droughts, as complex climatic hazards, can threaten livelihoods, economies, and ecosystems in low-lying island states. In extreme cases, drought may cripple national development in these countries, and produce long-term impacts that hinder national efforts to achieve the United Nations’ sustainable development goals. This study addresses rainfall trends, the frequency of droughts, La Nina influences and the relationship between rainfall and Sea Surface Temperature (SST) in the small Pacific country of Tuvalu. The study follows this order of approach: (1) examine observed rainfall time series for four meteorological stations across Tuvalu; (2) decompose observed rainfall time series and develop detrended rainfall time series; (3) evaluate and identify rainfall trends, including drought frequency; (4) define drought in Tuvalu using box plots; (5) evaluate the seasonal cycle of rainfall; (6) identify La Nina years and (7) test the correlation between SST, an indicator of La Nina events, and rainfall. The findings of this study revealed that (1) de-trended rainfall time series show declining trends in all four rainfall stations over the period 1953-2012; (2) the frequency of drought ranges from three to fourteen years with a mean of nine years; (3) the occurrence of drought appears to follow the La Nina years; (4) boxplots provide an effective option for defining drought and, finally, (5) there is empirical support for a moderate to strong correlation between the de-trended values of SST and rainfall in the area of study.

Construction of rainfall change scenarios over the Chilka Lagoon in India

The present study attempted to quantify long-term seasonal and annual rainfall change for the period 1901–2004 over the Chilka Lagoon in India, the second largest lagoon in the world using multiple gridded data sources. The future rainfall projection is also constructed using the four Representative Concentration Pathways (RCPs) of the Global Circulation Models (GCMs) of the Coupled Model Intercomparison Project phase 5 (CMIP5). Skill of GCMs to simulate the observed rainfall over the lagoon was investigated through estimation of long-term trends and comparison of mean seasonal cycles using Taylor diagram. Finally based on the combined results obtained through trend analysis as well as seasonal cycles, 12 better performing GCMs were selected. Ensemble mean of better performing GCMs reveal that the rainfall in annual, monsoon and winter seasons have increased in the last century similar to three observational gridded data sources. The projected seasonal cycle of rainfall from different RCPs shows a dipole like characteristics where the drier (winter) and moist (monsoon) seasons show a surplus of rainfall (11–25%) while the premonsoon and the postmonsoon seasons show a deficient rainfall (3–52%) at the end of 21st century. It is interesting to note that the Chilka Lake will expected to receive an increasing amount of annual rainfall by 3–7% in 2020s, 7–11% in 2050s and 10–21% in 2080s. Ensemble mean of future downscaled scenarios revealed that the annual rainfall will increase slightly higher rate as compared to without downscaling indicating high uncertainty in future projection.

Model simulations of rainfall over southern Africa and its eastern escarpment

Rainfall simulations over southern and tropical Africa in the form of low-resolution Atmospheric Model Intercomparison Project (AMIP) simulations and higher resolution National Centre for Environmental Prediction (NCEP) reanalysis downscalings are presented and evaluated in this paper. The model used is the conformal-cubic atmospheric model (CCAM), a variable-resolution global atmospheric model. The simulations are evaluated with regards to rainfall totals, spatial distribution, seasonality and inter-annual variability. Since both Global Circulation Models (GCMs) and Regional Climate Models (RCMs) are known to have relatively large biases and shortcomings in simulating rainfall over the steep eastern escarpment of southern Africa and in particular Lesotho, the paper has a focus on evaluating model performance over these regions. It is shown that in the reanalysis simulations the model realistically represents the seasonal cycle in rainfall. However, the AMIP simulations are prone to the model overestimating rainfall totals in spring. The spatial distribution of rainfall is simulated realistically; however rainfall totals are significantly overestimated over the escarpment areas of both southern Africa and East Africa. When nudged within the observed circulation patterns of the reanalysis data, the model is capable of realistically simulating inter-annual rainfall variability over the eastern parts of southern Africa.

Rainfall hotspots over the southern tropical Andes: Spatial distribution, rainfall intensity, and relations with large‐scale atmospheric circulation

AbstractThe Andes/Amazon transition is among the rainiest regions of the world and the interactions between large‐scale circulation and the topography that determine its complex rainfall distribution remain poorly known. This work provides an in‐depth analysis of the spatial distribution, variability, and intensity of rainfall in the southern Andes/Amazon transition, at seasonal and intraseasonal time scales. The analysis is based on comprehensive daily rainfall data sets from meteorological stations in Peru and Bolivia. We compare our results with high‐resolution rainfall TRMM‐PR 2A25 estimations. Hotspot regions are identified at low elevations in the Andean foothills (400–700 masl) and in windward conditions at Quincemil and Chipiriri, where more than 4000 mm rainfall per year are recorded. Orographic effects and exposure to easterly winds produce a strong annual rainfall gradient between the lowlands and the Andes that can reach 190 mm/km. Although TRMM‐PR reproduces the spatial distribution satisfactorily, it underestimates rainfall by 35% in the hotspot regions. In the Peruvian hotspot, exceptional rainfall occurs during the austral dry season (around 1000 mm in June–July–August; JJA), but not in the Bolivian hotspot. The direction of the low‐level winds over the Andean foothills partly explains this difference in the seasonal rainfall cycle. At intraseasonal scales in JJA, we found that, during northerly wind regimes, positive rainfall anomalies predominate over the lowland and the eastern flank of the Andes, whereas less rain falls at higher altitudes. On the other hand, during southerly regimes, rainfall anomalies are negative in the hotspot regions. The influence of cross‐equatorial winds is particularly clear below 2000 masl.