‘Eastern African paradox’ rainfall decline due to shorter not less intense long rains

An observed decline in the Eastern African Long Rains from the 1980s to late 2000s appears contrary to the projected increase under future climate change. This “Eastern African climate paradox” confounds use of climate projections for adaptation planning across Eastern Africa. Here we show the decline corresponds to a later onset and earlier cessation of the long rains, with a similar seasonal maximum in area-averaged daily rainfall. Previous studies have explored the role of remote teleconnections, but those mechanisms do not sufficiently explain the decline or the newly identified change in seasonality. Using a large ensemble of observations, reanalyses and atmospheric simulations, we propose a regional mechanism that explains both the observed decline and the recent partial recovery. A decrease in surface pressure over Arabia and warmer north Arabian Sea is associated with enhanced southerlies and an earlier cessation of the long rains. This is supported by a similar signal in surface pressure in many atmosphere-only models giving lower May rainfall and an earlier cessation. Anomalously warm seas south of Eastern Africa delay the northward movement of the tropical rain-band, giving a later onset. These results are key in understanding the paradox. It is now a priority to establish the balance of mechanisms that have led to these trends, which are partially captured in atmosphere-only simulations

Future changes in seasonality in Eastern Africa from regional simulations with explicit and parametrised convection

The Eastern Africa precipitation seasonal cycle is of significant societal importance, and yet the current generation of coupled global climate models fails to correctly capture this seasonality. The use of convective parametrisation schemes is a known source of precipitation bias in such models. Recently, a high-resolution regional model was used to produce the first pan-African climate change simulation that explicitly models convection. Here, this is compared with a corresponding parametrised-convection simulation, to explore the effect of the parametrisation on representation of Eastern Africa precipitation seasonality. Both models capture current seasonality, although an overestimate in September-October in the parametrised simulation leads to an early bias in the onset of the boreal autumn short rains, associated with higher convective instability and near-surface moist static energy. This bias is removed in the explicit model. Under future climate change both models show the short rains getting later and wetter. For the boreal spring long rains, the explicit convection simulation shows the onset advancing but the parametrised simulation shows little change. Over Uganda and western Kenya both simulations show rainfall increases in the January-February dry season, and large increases in boreal summer and autumn rainfall, particularly in the explicit convection model, changing the shape of the seasonal cycle, with potential for pronounced socio-economic impacts. Interannual variability is similar in both models. Results imply that parameterisation of convection may be a source of uncertainty for projections of changes in seasonal timing from global models, and that potentially impactful changes in seasonality should be highlighted to users

Extreme rainfall in East Africa October 2019 – January 2020 and context under future climate change

The 2019 October-December rains over East Africa were one of the wettest seasons on record, with many locations receiving more than double the climatological rainfall, leading to floods and landslides across the region. Above average rainfall continued into January 2020. The persistently high rainfall also contributed to the locust plagues that affected much of East Africa in January 2020. Wet conditions in East Africa are typically associated with El Nino and/or positive Indian Ocean Dipole events. In October-December 2019 a warm anomaly was present in the western Indian Ocean while a cool anomaly was present in the eastern Indian Ocean (a positive Indian Ocean Dipole); conditions known to give above average rainfall over East Africa. The warm anomaly in the western Indian Ocean persisted into January 2020. Seasonal and monthly forecasts correctly predicted above average rainfall during the October-December season. January rainfall is found to be correlated with sea surface temperatures over the western Indian Ocean. Climate model projections suggest that strong positive Indian Ocean Dipole events and wet October-December seasons may become more frequent under future climate change, with associated increased risks of floods

Representation of precipitation and top-of-atmosphere radiation in a multi-model convection-permitting ensemble for the Lake Victoria Basin (East-Africa)

The CORDEX Flagship Pilot Study ELVIC (climate Extremes in the Lake VICtoria basin) was recently established to investigate how extreme weather events will evolve in this region of the world and to provide improved information for the climate impact community. Here we assess the added value of the convection-permitting scale simulations on the representation of moist convective systems over and around Lake Victoria. With this aim, 10 year present-day model simulations were carried out with five regional climate models at both PARameterized (PAR) scales (12–25 km) and Convection-Permitting (CP) scales (2.5–4.5 km), with COSMO-CLM, RegCM, AROME, WRF and UKMO. Most substantial systematic improvements were found in metrics related to deep convection. For example, the timing of the daily maximum in precipitation is systematically delayed in CP compared to PAR models, thereby improving the agreement with observations. The large overestimation in the total number of rainy events is alleviated in the CP models. Systematic improvements were found in the diurnal cycle in Top-Of-Atmosphere (TOA) radiation and in some metrics for precipitation intensity. No unanimous improvement nor deterioration was found in the representation of the spatial distribution of total rainfall and the seasonal cycle when going to the CP scale. Furthermore, some substantial biases in TOA upward radiative fluxes remain. Generally our analysis indicates that the representation of the convective systems is strongly improved in CP compared to PAR models, giving confidence that the models are valuable tools for studying how extreme precipitation events may evolve in the future in the Lake Victoria basin and its surroundings

Scientific understanding of East African climate change from the HyCRISTAL project

Integrating Hydro-Climate Science into Policy Decisions for Climate-Resilient Infrastructure and Livelihoods in East Africa (HyCRISTAL) is a Future Climate for Africa (FCFA) project funded to deliver new understanding of East African climate change and its impacts, and to demonstrate use of climate change information in long-term decision-making in the region. Here, we briefly summarise key findings from HyCRISTAL so far on climate change, as well as key findings from the pan-African FCFA project “IMPALA” relevant to East Africa, both in the context of previous literature on the topic

Scientific understanding of East African climate change from the HyCRISTAL project

Integrating Hydro-Climate Science into Policy Decisions for Climate-Resilient Infrastructure and Livelihoods in East Africa (HyCRISTAL) is a Future Climate for Africa (FCFA) project funded to deliver new understanding of East African climate change and its impacts, and to demonstrate use of climate change information in long-term decision-making in the region. Here, we briefly summarise key findings from HyCRISTAL so far on climate change, as well as key findings from the pan-African FCFA project “IMPALA” relevant to East Africa, both in the context of previous literature on the topic

The effect of explicit convection on couplings between rainfall, humidity and ascent over Africa under climate change

The Hadley circulation and tropical rain belt are dominant features of African climate. Moist convection provides ascent within the rain belt, but must be parameterized in climate models, limiting predictions. Here, we use a pan-African convection-permitting model (CPM), alongside a parameterized convection model (PCM), to analyze how explicit convection affects the rain belt under climate change. Regarding changes in mean climate, both models project an increase in total column water (TCW), a widespread increase in rainfall, and slowdown of subtropical descent. Regional climate changes are similar for annual mean rainfall but regional changes of ascent typically strengthen less or weaken more in the CPM. Over a land-only meridional transect of the rain belt, the CPM mean rainfall increases less than in the PCM (5% vs 14%) but mean vertical velocity at 500 hPa weakens more (17% vs 10%). These changes mask more fundamental changes in underlying distributions. The decrease in 3-hourly rain frequency and shift from lighter to heavier rainfall are more pronounced in the CPM and accompanied by a shift from weak to strong updrafts with the enhancement of heavy rainfall largely due to these dynamic changes. The CPM has stronger coupling between intense rainfall and higher TCW. This yields a greater increase in rainfall contribution from events with greater TCW, with more rainfall for a given large-scale ascent, and so favors slowing of that ascent. These findings highlight connections between the convective-scale and larger-scale flows and emphasize that limitations of parameterized convection have major implications for planning adaptation to climate change

Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Introduction: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. Methods: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. Findings: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5–5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4–10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32–4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23–11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. Interpretation: After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification

Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques

Article Open Access Published: 08 November 2019 Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques Amirhossein Mostajabi, Declan L. Finney, Marcos Rubinstein & Farhad Rachidi npj Climate and Atmospheric Science volume 2, Article number: 41 (2019) Cite this article Article metrics 71 Altmetric Metrics details Abstract Lightning discharges in the atmosphere owe their existence to the combination of complex dynamic and microphysical processes. Knowledge discovery and data mining methods can be used for seeking characteristics of data and their teleconnections in complex data clusters. We have used machine learning techniques to successfully hindcast nearby and distant lightning hazards by looking at single-site observations of meteorological parameters. We developed a four-parameter model based on four commonly available surface weather variables (air pressure at station level (QFE), air temperature, relative humidity, and wind speed). The produced warnings are validated using the data from lightning location systems. Evaluation results show that the model has statistically considerable predictive skill for lead times up to 30 min. Furthermore, the importance of the input parameters fits with the broad physical understanding of surface processes driving thunderstorms (e.g., the surface temperature and the relative humidity will be important factors for the instability and moisture availability of the thunderstorm environment). The model also improves upon three competitive baselines for generating lightning warnings: (i) a simple but objective baseline forecast, based on the persistence method, (ii) the widely-used method based on a threshold of the vertical electrostatic field magnitude at ground level, and, finally (iii) a scheme based on CAPE threshold. Apart from discussing the prediction skill of the model, data mining techniques are also used to compare the patterns of data distribution, both spatially and temporally among the stations. The results encourage further analysis on how mining techniques could contribute to further our understanding of lightning dependencies on atmospheric parameters