Search

Mesoscale Convective Systems (MCSs) play a critical role in tropical rainfall patterns and circulations. To reduce persistent biases and improve understanding of the climate system, international groups have called for unprecedented investment in global convection–permitting (CP) climate models. It is essential such models accurately represent MCSs, and in particular environmental interactions such as dynamical control by wind shear. We show that in representative current generation CP simulations, MCS updraft entrainment decreases with shear, leading to a realistic increase of extreme rainfall. We find the control of environmental shear extends to mean storm rainfall and anvil heights. The simulation of these effects depends strongly on model physics in both CP and parameterized models. We show that in West Africa, MCS shear response influences the zonal distribution of storm diabatic heating, modifying upscale impacts of convection. Our results demonstrate key tests for focused process–based assessment of CP model fidelity. Plain Language Summary: Movements of tropical air are driven by interactions between large, continental–scale circulations and localized deep convection — cells of ascending moist air reaching to the tropopause. These cells can aggregate to form Mesoscale Convective Systems (MCSs), long–lived thunderstorm clusters with large cold cloud shields that dominate tropical rainfall and atmospheric heating, forcing air circulations. Meanwhile, the strength of MCS rainfall depends on local atmospheric conditions. Such local process interactions occur on scales much smaller than typical global climate model grid spacings (>25 km), thus requiring a sub–grid parameterization of deep convection that introduces model biases. Here we show that kilometer–scale models can realistically capture the response of MCSs to local atmospheric moisture and wind conditions. A realistic response to the wind field relies on models showing variable mixing of dry environmental air into convective updrafts, with reduced rates for stronger vertical wind shear. We find that this wind (i.e., dynamical) control on MCSs explains patterns of mean rainfall biases in West Africa and influences atmospheric heating, likely mediating MCSs' upscale impacts on tropical circulations. Our results provide vital process–based methods for verifying the added value of explicitly representing convective storms in novel kilometer–scale global models. Key Points: Convection‐permitting models can capture the response of Mesoscale Convective Systems to both moisture and shear in West AfricaStrong shear gives larger cores, reduced entrainment and greater maximum and mean storm rainfall; this is missed in a parameterized modelShear response has upscale impacts through control of zonal distribution of biases in storm mean rainfall and diabatic heating [ABSTRACT FROM AUTHOR]

Simulating the West African monsoon (WAM) system using numerical weather and climate models suffers from large uncertainties, which are difficult to assess due to nonlinear interactions between different components of the WAM. Here we present a fundamentally new approach to the problem by approximating the behavior of a numerical model – here the Icosahedral Nonhydrostatic (ICON) model – through a statistical surrogate model based on universal kriging, a general form of Gaussian process regression, which allows for a comprehensive global sensitivity analysis. The main steps of our analysis are as follows: (i) identify the most important uncertain model parameters and their probability density functions, for which we employ a new strategy dealing with non-uniformity in the kriging process. (ii) Define quantities of interest (QoIs) that represent general meteorological fields, such as temperature, pressure, cloud cover and precipitation, as well as the prominent WAM features, namely the tropical easterly jet, African easterly jet, Saharan heat low (SHL) and intertropical discontinuity. (iii) Apply a sampling strategy with regard to the kriging method to identify model parameter combinations which are used for numerical modeling experiments. (iv) Conduct ICON model runs for identified model parameter combinations over a nested limited-area domain from 28° W to 34° E and from 10° S to 34° N. The simulations are run for August in 4 different years (2016 to 2019) to capture the peak northward penetration of rainfall into West Africa, and QoIs are computed based on the mean response over the whole month in all years. (v) Quantify sensitivity of QoIs to uncertain model parameters in an integrated and a local analysis. The results show that simple isolated relationships between single model parameters and WAM QoIs rarely exist. Changing individual parameters affects multiple QoIs simultaneously, reflecting the physical links between them and the complexity of the WAM system. The entrainment rate in the convection scheme and the terminal fall velocity of ice particles show the greatest effects on the QoIs. Larger values of these two parameters reduce cloud cover and precipitation and intensify the SHL. The entrainment rate primarily affects 2 m temperature and 2 m dew point temperature and causes latitudinal shifts, whereas the terminal fall velocity of ice mostly affects cloud cover. Furthermore, the parameter that controls the evaporative soil surface has a major effect on 2 m temperature, 2 m dew point temperature and cloud cover. The results highlight the usefulness of surrogate models for the analysis of model uncertainty and open up new opportunities to better constrain model parameters through a comparison of the model output with selected observations. [ABSTRACT FROM AUTHOR]

East Africa is highly vulnerable to extreme weather events, such as droughts and floods. Skillful seasonal forecasts exist for the October–November–December short rains, enabling informed decisions, whereas seasonal forecasts for the March–April–May (MAM) long rains have historically had low skill, limiting preparation capacity. Therefore, improved long rains prediction is a high priority and would contribute to climate change resilience in the region. Recent work has highlighted how lower‐troposphere Congo zonal winds in MAM strongly impact regional moisture fluxes and the long rains total precipitation. We therefore approach long rains predictability through the predictability of the Congo winds. We analyze a set of hindcasts from a dynamical prediction system that is able to reproduce the long rains—Congo winds relationship in its individual ensemble members. Encouragingly, in observations, the strength of MAM Congo zonal winds and East Africa rainfall show substantial correlation with the MAM Atlantic (including North Atlantic Oscillation, NAO) and Indo‐Pacific variability, suggestive of ocean influence and potential predictability. However, these features are replaced by different teleconnections in the hindcast ensemble mean fields. This is also true for NAO linkage to Congo winds, despite correct representation in individual members, and good skill in hindcasting the NAO itself. The net effect is strongly negative skill for the Congo winds. We explore statistical correction methods, including using the Congo zonal wind as an anchor index in a signal‐to‐noise calibration for the long rains. This is considered a demonstration of concept, for subsequent implementation using models with better Congo zonal wind skill. Indeed, the clear signals found in the Atlantic (including Mediterranean) and Indo‐Pacific, studied here both in observations and a dynamical prediction system, motivate evaluation of these features across other prediction systems, and offer the prospect of improved physically‐informed long rains dynamical predictions. [ABSTRACT FROM AUTHOR]

Precipitation across East Africa shows marked interannual variability. Seasonal forecast skill for the OND short rains is significantly higher than for the MAM long rains, which also exhibit poorly understood decadal variability. On sub‐seasonal time‐scales rainfall is influenced strongly by the phase of the Madden–Julian Oscillation (MJO); here we investigate whether this influence extends to interannual and decadal scales. We show that the number of days that the MJO is active and in phases 1–3 has a greater influence than the mean amplitude of the MJO on interannual long rains variability (ρ = 0.59 for the count of phases 1–3, compared to ρ = 0.40 for amplitude). The frequency of these days is linked to a newly identified gradient in Pacific sea‐surface temperatures (SSTs), whose influence on long rains variability we show is itself mediated by the MJO. We develop a statistical model estimating East African rainfall from MJO state, and show that the influence of the MJO on seasonal rainfall extends to the short rains, and to a lesser extent also into January and February. Our results show the importance of capturing the SST‐MJO phase relationship in models used for predictions of East African rainfall across time‐scales, and motivate investigating this further. [ABSTRACT FROM AUTHOR]

However, due to late dropouts, the Ghana Cocoa team lost its African representative and a UK Post-Doctoral Research Associate, so was very unbalanced, and we ended up with the two Ghana teams merging into one larger-than-ideal team (GhanaAg team). The Leeds Africa Climate Hackathon aimed to generate user-relevant narratives of possible future climate in East and West Africa relevant to hydroelectric power generation and agriculture respectively. Analysis of CMIP5 models suggests West Africa will see shorter wet seasons, increasing rainfall intensity, and decreasing rainfall frequency under climate change (Dunning I et al i ., [6]). The team hypothesised that the dam would not be able to retain these higher intensity rains and would have to release them downstream, which in turn could increase periods when dam levels were low even if decadal rainfall were similar to today. [Extracted from the article]