AbstractBackgroundEthiopia has a history of climate related malaria epidemics. An improved understanding of malaria–climate interactions is needed to inform malaria control and national adaptation plans.MethodsMalaria–climate associations in Ethiopia were assessed using (a) monthly climate data (1981–2016) from the Ethiopian National Meteorological Agency (NMA), (b) sea surface temperatures (SSTs) from the eastern Pacific, Indian Ocean and Tropical Atlantic and (c) historical malaria epidemic information obtained from the literature. Data analysed spanned 1950–2016. Individual analyses were undertaken over relevant time periods. The impact of the El Niño Southern Oscillation (ENSO) on seasonal and spatial patterns of rainfall and minimum temperature (Tmin) and maximum temperature (Tmax) was explored using NMA online Maprooms. The relationship of historic malaria epidemics (local or widespread) and concurrent ENSO phases (El Niño, Neutral, La Niña) and climate conditions (including drought) was explored in various ways. The relationships between SSTs (ENSO, Indian Ocean Dipole and Tropical Atlantic), rainfall, Tmin, Tmax and malaria epidemics in Amhara region were also explored.ResultsEl Niño events are strongly related to higher Tmax across the country, drought in north-west Ethiopia during the July–August–September (JAS) rainy season and unusually heavy rain in the semi-arid south-east during the October–November–December (OND) season. La Niña conditions approximate the reverse. At the national level malaria epidemics mostly occur following the JAS rainy season and widespread epidemics are commonly associated with El Niño events when Tmax is high, and drought is common. In the Amhara region, malaria epidemics were not associated with ENSO, but with warm Tropical Atlantic SSTs and higher rainfall.ConclusionMalaria–climate relationships in Ethiopia are complex, unravelling them requires good climate and malaria data (as well as data on potential confounders) and an understanding of the regional and local climate system. The development of climate informed early warning systems must, therefore, target a specific region and season when predictability is high and where the climate drivers of malaria are sufficiently well understood. An El Niño event is likely in the coming years. Warming temperatures, political instability in some regions, and declining investments from international donors, implies an increasing risk of climate-related malaria epidemics.

In this study, three regional climate models (RCMs), CCLM5‐0‐15, RegCM4‐7 and REMO2015, from CORDEX‐CORE (AFR‐22) are evaluated in their ability to reproduce rainfall variability in Rwanda for the period 1981–2005. They are driven by three different global climate models (GCMs), namely MPI‐M‐MPI‐ESM‐LR, NCC‐NorESM1‐M and MOHC‐HadGEM2‐ES, and the European Centre for Medium‐Range Weather Forecasts Reanalysis (ECMWF‐ERAINT). Simulated rainfall is evaluated against observations from Rwanda Meteorology Agency to assess models' performance. A set of metrics are used to quantify discrepancies of models' simulations from observations. A possible association of El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) to rainfall over Rwanda is investigated. It is found that in general, all RCMs, their ensemble and multimodel ensemble means reproduce satisfactorily the spatial distribution of the mean seasonal rainfall (MSR), the mean rainfall annual cycle, and the interannual variability of the MSR for both March–April–May (MAM) and October–November–December (OND). However, significant biases in individual RCMs are observed with varying magnitude of bias in space. Observed MSR indicates a positive trend of 0.045 and 0.058 mm·day·year−1, respectively, for MAM and OND at 0.05 significance level, but almost all models indicate no significant trend (at 0.05 significance level). The seasonal correlations between observed rainfall anomalies and sea surface temperature (SST) anomalies indices across the tropical Pacific (Niño1+2 and Niño3.4) and Indian Oceans associated, respectively, with ENSO and IOD, although relatively weak, are reproduced by the three RCMs driven by ECMWF‐ERAINT and the multimodel ensemble means of ECMWF‐ERAINT and MPI‐M‐MPI‐ESM‐LR. Analysis of the Taylor diagram indicates that CCLM5‐0‐15_MPI‐M‐MPI‐ESM‐LR and the multimodel ensemble mean of MPI‐M‐MPI‐ESM‐LR outperform individual models. Overall, the evaluation finds reasonable model skill in representing seasonal rainfall climatology and variability, suggesting the potential use of CORDEX‐CORE (AFR‐22) RCMs for the assessment of future climate projections in Rwanda.

In recent years, there has been increasing demand for high-resolution seasonal climate forecasts at sufficient lead times to allow response planning from users in agriculture, hydrology, disaster risk management, and health, among others.This paper examines the forecasting skill of the North American Multi-model Ensemble (NMME) over Ethiopia during the June to September (JJAS) season.The NMME, one of the multi-model seasonal forecasting systems, regularly generates monthly seasonal rainfall forecasts over the globe with 0.5 -11.5 months lead time.The skill and predictability of seasonal rainfall are assessed using 28 years of hindcast data from the NMME models.The forecast skill is quantified using canonical correlation analysis (CCA) and root mean square error.The results show that the NMME models capture the JJAS seasonal rainfall over central, northern, and northeastern parts of Ethiopia while exhibiting weak or limited skill across western and southwestern

Seasonal rainfall in Senegal, and across the Sahel region of Africa, is very important for water provision, agricultural and pastoral productivity and food security.This study analyzes several July-September seasonal rainfall forecast systems for Senegal using daily observed rainfall from the high spatial resolution (4km), gridded, merged satellite-station dataset from the Enhancing NAtional ClimaTe Services (ENACTS). The objective forecast systems are based on statistical downscaling of individual and multi-model output for several variables (model rainfall, SST and winds) from the North American Multi-Model Ensemble (NMME) and the European Copernicus (C3S) suites using the recently developed Python-based Climate Predictability Tool (PyCPT). The skill of the candidate predictors is compared using the Pearson and Spearman correlations and the Rank Probability Skill Score (RPSS). Forecast skill is also evaluated at multiple lead times for the best candidate predictor. Further work needs to be done to explore other critical rainfall characteristics (onset date, dry spell length).Results show significant positive skill at a ~4 km resolution (relevant for local decision making) at a lead time of four to six months. Model NMME rainfall and model EU-C3S 850 mb winds over a relatively small domain centered over Senegal (5-25 N and 5-25 W) are promising candidate predictors. These calibrated forecasts are framed in a flexible, probabilistic manner and are designed to be user-adaptable.This forecasting approach could potentially help address the objectives of the WMO/USAID sponsored Climate Services for Increased Resilience in the Sahel project and Senegal’s National Framework for Climate Services.

Predictability of Ethiopian Kiremt rainfall (June to September: JJAS) and forecast skill of the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation seasonal forecast system 5 (SEAS5) is explored during 1981–2019. The first empirical orthogonal function of observed rainfall explains 50.6% of the total variability and is characterized by positive rainfall anomalies largely confined over the northwestern and central-western regions of Ethiopia. Consequently, a Kiremt rainfall index (KRI) is defined for this region. The correlation coefficient (CC) between the observed and predicted KRI is 0.68 and 0.53 for May and April starts, respectively. Composite analysis of sea surface temperature (SST) and lower-level circulation based on excess and deficit years of Kiremt rains shows that the El Niño Southern–Oscillation is the main modulator of the Kiremt rainfall variability. The CC between KRI and Niño3.4 index is − 0.62, indicating that El Niño is accompanied by below-normal Kiremt rainfall, while La Niña is accompanied by above-normal amounts. The fifth generation of ECMWF atmospheric reanalysis (ERA5) shows that excess (deficit) Kiremt rainfall anomalies are associated with an anomalous low (high) pressure centered over northeast Arabian Peninsula and an anomalous in-phase (reverse) low-level Somali Jet. SEAS5 reproduces the spatial and temporal components of observed Kiremt rainfall variability, including the main climatic features associated with excess and deficit Kiremt rainfall in May and April starts. However, certain important observed features like above-normal SSTs in the Gulf of Guinea are not well predicted. Results indicate that Kiremt rains has some potential predictability and SEAS5 shows a moderate forecast skill. Probabilistic analysis shows highest values where predictability and deterministic skill are also highest.

Previous studies revealed that many areas in Africa experienced an apparent warming rate in surface temperature in the last century. However, the contributing factors have not been investigated in details. In the present study, natural and anthropogenic forcings accountable for surface temperature variability and change are examined from the historical Coupled Model Inter-comparison Project phase six (CMIP6) simulations and future projections under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2.4.5 and SSP5-8.5), which represent low, moderate and high emission scenarios, respectively. Results indicate that from 1901 to 2014, surface temperature has increased by similar to 0.07 degrees C/decade over both Eastern Africa (EAF) and Sahara (SAH) regions, 0.06 degrees C/decade in Southern Africa (SAF) and Western Africa (WAF) regions and follows the global warming trend. It is found that Greenhouse Gases (GHG) and Land-Use (LU) change are the leading contributors to the observed warming in historical surface temperature over Africa. Anthropogenic Aerosols (AA) show a cooling effect on surface temperature. Under both SSP1-2.6 and SSP2-4.5 emission scenarios, the surface temperature increases to 2059 and declines afterwards. On the other hand, under SSP5-8.5, the surface temperature is expected to increase throughout the 21st century. The impacts of warming will be hard-felt in SAH and SAF regions compared to other areas. The analysis of rare hot and cold events (2080-2099) based on the 20-year annual highest and lowest daily surface temperature relative to the recent past (1995-2014) under SSP2-4.5 indicates that both events are likely to increase significantly in the later 21st century. Nevertheless, proper management of Land use and control of anthropogenic factors (GHGs and AA) may lead to a substantial reduction in further warming over Africa.

There were a large number of active meteorological stations in Rwanda prior to the mid‐1990s and since around 2010. However, from around the time of the Rwandan genocide in 1994 throughout the late 2000s, the number of active stations was greatly reduced. To address temporal and spatial gaps in meteorological observation in several African nations (including Rwanda), the ENACTS (Enhancing National Climate Services) initiative reconstructs rainfall and temperature data by combining station data with satellite rainfall estimates, and with reanalysis products for temperature. Bias correction factors are applied to the satellite and reanalysis data and the merged final product is spatiotemporally complete from the early 1980s to the present at a high spatial resolution (4–5 km). This paper offers the first analysis of Rwanda's climatology using this new ENACTS data set for 1981–2016.The temperature and rainfall climatology of Rwanda are analysed at both annual and seasonal timescales as are the climatological influences of topography and regional winds. Climatology maps of mean rainfall intensity, rainy day, 5‐day dry spell and extreme rain day (20+mm) frequency are shown, and spatial pattern correlations are analysed.The rainfall climatology of Rwanda exhibits a clear seasonal bimodality typical of the East Africa region. Topography has a significant effect with the more mountainous, higher‐elevation western part of the country being consistently cooler and wetter than the lower, flatter eastern region. Southeasterly winds tend to prevail over Rwanda, but in some seasons, the climatological winds weaken and shift direction. While spatial patterns of rainy day and dry spell frequency are consistent with the spatial patterns of the seasonal rainfall total, climatologically drier regions have a higher mean rainfall intensity on rainy days. This analysis demonstrates the value of the ENACTS product and illustrates climatological patterns in Rwanda over the last 30 years.

How much should the present generations sacrifice to reduce emissions today, in order to reduce the future harms of climate change? Within climate economics, debate on this question has been focused on so-called "ethical parameters" of social time preference and inequality aversion. We show that optimal climate policy similarly importantly depends on the future of the developing world. In particular, although global poverty is falling and the economic lives of the poor are improving worldwide, leading models of climate economics may be too optimistic about two central predictions: future population growth in poor countries, and future convergence in total factor productivity (TFP). We report results of small modifications to a standard model: under plausible scenarios for high future population growth (especially in sub-Saharan Africa) and for low future TFP convergence, we find that optimal near-term carbon taxes could be substantially larger.

Integrated assessment models (IAMs) of climate and the economy provide estimates of the social cost of carbon and inform climate policy. With the Nested Inequalities Climate Economy model (NICE) (Dennig et al. PNAS 112:15,827–15,832, 2015), which is based on Nordhaus's Regional Integrated Model of Climate and the Economy (RICE), but also includes inequalities within regions, we investigate the comparative importance of several factors—namely, time preference, inequality aversion, intraregional inequalities in the distribution of both damage and mitigation cost and the damage function. We do so by computing optimal carbon price trajectories that arise from the wide variety of combinations that are possible given the prevailing range of disagreement over each factor. This provides answers to a number of questions, including Thomas Schelling's conjecture that properly accounting for inequalities could lead the inequality aversion parameter to have an effect opposite to what is suggested by the Ramsey equation.

Future population growth is uncertain and matters for climate policy: higher growth entails more emissions and means more people will be vulnerable to climate-related impacts. We show that how future population is valued importantly determines mitigation decisions. Using the Dynamic Integrated Climate-Economy model, we explore two approaches to valuing population: a discounted version of total utilitarianism (TU), which considers total wellbeing and is standard in social cost of carbon dioxide (SCC) models, and of average utilitarianism (AU), which ignores population size and sums only each time period's discounted average wellbeing. Under both approaches, as population increases the SCC increases, but optimal peak temperature decreases. The effect is larger under TU, because it responds to the fact that a larger population means climate change hurts more people: for example, in 2025, assuming the United Nations (UN)-high rather than UN-low population scenario entails an increase in the SCC of 85% under TU vs. 5% under AU. The difference in the SCC between the two population scenarios under TU is comparable to commonly debated decisions regarding time discounting. Additionally, we estimate the avoided mitigation costs implied by plausible reductions in population growth, finding that large near-term savings ($billions annually) occur under TU; savings under AU emerge in the more distant future. These savings are larger than spending shortfalls for human development policies that may lower fertility. Finally, we show that whether lowering population growth entails overall improvements in wellbeing-rather than merely cost savings-again depends on the ethical approach to valuing population.

aWoodrow Wilson School, Princeton University, Princeton, NJ 08544; bDepartment of Philosophy, University of Vermont, Burlington, VT 05405; cYale–NUS College, Singapore 138527; dCenter for Human Values, Princeton University, Princeton, NJ 08544; eInternational Research Institute for Climate and Society, Columbia University, Palisades, NY 10964; fDepartment of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08544; gDepartment of Economics, University of Texas at Austin, Austin, TX 78712; hEconomics and Planning Unit, Indian Statistical Institute, Delhi, India, 110016; iAndlinger Center for Energy and the Environment, Princeton University, Princeton, NJ 08544; and jInternational Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria A-2361

Index insurance has been viewed as a financial adaptation to climate risks with the potential for widespread application, especially in a developing world context. The potential for index insurance is explored in the context of hypothetical drought and flood contracts at the national level for farmers in the West African Sahel nations of Niger, Burkina Faso, and Mali. The region's climatology and dynamics are discussed and multiple datasets are considered as potential indices.Agricultural, precipitation, streamflow, remotely sensed vegetation, and Nino sea surface temperature indices were explored as potential bases for index insurance contract. Correlation analyses between the potential geophysical and agricultural indices are examined and two of the rainfall datasets are found to have robust positive correlations with millet production in all three nations, while a particular streamflow index is found to have a robust negative correlation with rice production in Niger. A methodological innovation of this research is the use of Gerrity skill score (GSS) analysis to analyze the indices of high correlation. The correlation and GSS analyses presented here indicate the potential for index insurance using two of the rainfall datasets for the millet crop (drought risk) of all three nations and the Niamey flood month streamflow dataset for the rice crop of Niger (flood risk).

Introduction: Climate-economy models known as 'integrated assessment models' are widely used by governments to inform climate policy, including through the estimation of the social cost of carbon. These models produce "optimal" mitigation trajectories by analyzing trade-offs between investing in GHG reduction and the occurrence of climate damages, both of which incur costs, but at different time points. To date, integrated assessment models of optimal global emissions reductions do not account for potential health benefits of mitigation in their optimization. Methods: We modified the multi-region RICE model by formulating a four-step feedback mechanism whereby reducing CO2 also improves air quality, which reduces mortality – a monetizable benefit that in turn increases utility. First, based on ECLIPSE scenarios, we estimated the expected marginal reductions in PM2.5 precursor emissions (SO2, NOx, PM2.5) attributable to a unit reduction in CO2. Second, we estimated how emission reductions reduced PM2.5 exposure using a regression based on historical data. Third, we calculated associated health benefits, in life-years gained, using linear exposure-response functions for all-cause mortality. And fourth, we monetized the life-years gained, which then fed back into model's optimization procedure. The aerosol emissions in the model's health module were consistent with the climate module. Results: Air quality improvements attributable to CO2 mitigation lead to moderate health benefits. Benefits are largest in India and China and occur mainly in the next forty years. The benefits incentivize near-term mitigation by shifting the "optimal" mitigation trajectory towards a higher carbon price in the coming decades. Conclusion: We developed the first integrated assessment model capable of accounting for the non-climate health benefits of CO2 mitigation. This new feature incentivizes more rapid mitigation, as benefits are experienced in the near-term.