A new database of the Entomological Inoculation Rate (EIR) is used to directly link the risk of infectious mosquito bites to climate in Sub-Saharan Africa. Applying a statistical mixed model framework to high-quality monthly EIR measurements collected from field campaigns in Sub-Saharan Africa, we analyzed the impact of rainfall and temperature seasonality on EIR seasonality and determined important climate drivers of malaria seasonality across varied climate settings in the region. We observed that seasonal malaria transmission requires a temperature window of 15-40 degrees Celsius and is sustained if average temperature is well above the minimum or below the maximum temperature threshold. Our study also observed that monthly maximum rainfall for seasonal malaria transmission should not exceed 600 mm in west Central Africa, and 400 mm in the Sahel, Guinea Savannah and East Africa. Based on a multi-regression model approach, rainfall and temperature seasonality were significantly associated with malaria seasonality in most parts of Sub-Saharan Africa except in west Central Africa. However, areas characterized by significant elevations such as East Africa, topography has a significant influence on which climate variable is an important determinant of malaria seasonality. Malaria seasonality lags behind rainfall seasonality only at markedly seasonal rainfall areas such as Sahel and East Africa; elsewhere, malaria transmission is year-round. The study’s outcome is important for the improvement and validation of weather-driven dynamical malaria models that directly simulate EIR. It can contribute to the development of malaria models fit-for-purpose to support health decision-making towards malaria control or elimination in Sub-Saharan Africa.

We introduce a minimal stochastic lattice model for the column relative humidity ($R$) in the tropics, which incorporates convective moistening, lateral mixing and subsidence drying. The probability of convection occurring in a location increases with $R$, based on TRMM observations, providing a positive feedback that could lead to aggregation. We show that the simple model reproduces many aspects of full-physics cloud resolving model experiments. Depending on model parameter settings and domain size and resolution choices, it can produce both random or aggregated equilibrium states. Clustering occurs more readily with larger domains and coarser resolutions, in agreement with full physics models. Using dimensional arguments and fits from empirical data, we derive a dimensionless parameter we call the aggregation number, $N_{ag}$, that predicts whether a specific model and experiment setup will result in an aggregated or random state. The parameter includes the moistening feedback strength, the diffusion, the subsidence timescale, the domain size and spatial resolution. Using large ensembles of experiments, we show that the transition between random and aggregated states occurs at a critical value of $N_{ag}$. We argue that $N_{ag}$ could help to understand the differences in aggregation states between full physics, cloud resolving models.

We investigate how interactive surface feedbacks impact convective clustering in a cloud resolving model coupled to a slab ocean, where the domain-mean temperature is controlled with an adaptive Q-flux. In the first investigation, with constant domain-mean surface temperature, progressively thinner ocean layers slow the onset of clustering by up to a month through surface feedback. Enhanced solar radiation in nascent dry, cloud-free areas leads to local surface warming, increasing latent heat fluxes in regions distant from convection. Once the magnitude of humidity anomalies increases, longwave emission dominates and the ocean cools rapidly under the dry patches. In this stage the surface feedback reverses and favors clustering. In the second investigation using a 1 meter mixed layer, the ocean undergoes a diurnal cycle in response to solar forcing, with a diurnal range of 2.5$^o$C in the domain mean. This leads to convective rainfall shifting from a weak broad nocturnal maximum to a sharper afternoon peak. The consequent daytime anvil shielding reduces the spatial SST variance, but the sharper rainfall peak results from more concurrent convective towers, which are distributed throughout the domain due to coldpools. This reduces the upper tropospheric water vapor variance and causes a slight delay in clustering onset, despite the reduced spatial SST variance. We find that the onset time is deterministic when strong forcing causes clustering to occur quickly (after 25 days), whereas it is highly stochastic when surface feedback weakens the diabatic forcing and delays clustering past 40 days.