Modelling rainfall erosivity using the Weather Research and Forecasting model

Soil erosion is a serious threat to agricultural productivity and the sustainable provision of food to a growing world population. Current erosion models employ simplistic treatments of rainfall. This thesis presents a new approach to erosion modelling, using the Weather Research and Forecasting model to simulate rainfall erosivity, an indicator of the erosive capacity of rain. Rainfall erosivity is modelled in the Caucasus region, an area vulnerable to erosion and climate change pressures. Low intensity rainfall (below 2 mmhr^{-1}) is found to contribute significantly to erosivity (23%), contrary to common assumptions. An exponential dependence of the fraction of erosivity from light rain on the proportion of light rain is found. Erosion models focus on storms, but results suggest that storm-based calculations may exclude up to 30% of erosivity. In the Universal Soil Loss Equation, this does not lead to errors in long term soil loss but could cause an underestimation of event erosion. Rainfall kinetic energy flux is an important variable in erosion prediction and is routinely parameterised from intensity. Here this is dynamically simulated from basic physics in a cloud resolving model, using four microphysics schemes. Results are within the range of observations and capture the observed variability in kinetic energy for a given intensity, where current methods fail. Large raindrops are shown to contribute disproportionately to total kinetic energy, and also to surface precipitation, compared with their number. No connection has hitherto been drawn between aerosols and soil erosion. The effect of aerosols on rainfall erosivity is investigated in a cloud resolving model. Aerosols can either enhance or suppress precipitation. In both these cases the response of erosivity to a rise in aerosols is in the same direction as, but amplified beyond, the change in total rain. It is also shown that aerosols can influence erosivity by changing raindrop sizes. These results suggest that anthropogenic aerosol emissions affect erosivity and thus may have important consequences for agricultural productivity.Open Acces

Climate-proofing a malaria eradication strategy

Two recent initiatives, the World Health Organization (WHO) Strategic Advisory Group on Malaria Eradication and the Lancet Commission on Malaria Eradication, have assessed the feasibility of achieving global malaria eradication and proposed strategies to achieve it. Both reports rely on a climate-driven model of malaria transmission to conclude that long-term trends in climate will assist eradication efforts overall and, consequently, neither prioritize strategies to manage the effects of climate variability and change on malaria programming. This review discusses the pathways via which climate affects malaria and reviews the suitability of climate-driven models of malaria transmission to inform long-term strategies such as an eradication programme. Climate can influence malaria directly, through transmission dynamics, or indirectly, through myriad pathways including the many socioeconomic factors that underpin malaria risk. These indirect effects are largely unpredictable and so are not included in climate-driven disease models. Such models have been effective at predicting transmission from weeks to months ahead. However, due to several well-documented limitations, climate projections cannot accurately predict the medium- or long-term effects of climate change on malaria, especially on local scales. Long-term climate trends are shifting disease patterns, but climate shocks (extreme weather and climate events) and variability from sub-seasonal to decadal timeframes have a much greater influence than trends and are also more easily integrated into control programmes. In light of these conclusions, a pragmatic approach is proposed to assessing and managing the effects of climate variability and change on long-term malaria risk and on programmes to control, eliminate and ultimately eradicate the disease. A range of practical measures are proposed to climate-proof a malaria eradication strategy, which can be implemented today and will ensure that climate variability and change do not derail progress towards eradication

Building climate-sensitive nutrition programmes

The food system and climate are closely interconnected. Although most research has focused on the need to adopt a plant-based diet to help mitigate climate change, there is also an urgent need to examine the effects of climate change on food systems to adapt to climate change. A systems approach can help identify the pathways through which climate influences food systems, thereby ensuring that programmes combating malnutrition take climate into account. Although little is known about how climate considerations are currently incorporated into nutrition programming, climate information services have the potential to help target the delivery of interventions for at-risk populations and reduce climate-related disruption during their implementation. To ensure climate services provide timely information relevant to nutrition programmes, it is important to fill gaps in our knowledge about the influence of climate variability on food supply chains. A proposed roadmap for developing climate-sensitive nutrition programmes recommends: (i) research aimed at achieving a better understanding of the pathways through which climate influences diet and nutrition, including any time lags; (ii) the identification of entry points for climate information into the decision-making process for nutrition programme delivery; and (iii) capacity-building and training programmes to better equip public health practitioners with the knowledge, confidence and motivation to incorporate climate resilience into nutrition programmes. With sustained investment in capacity-building, data collection and analysis, climate information services can be developed to provide the data, analyses and forecasts needed to ensure nutrition programmes target their interventions where and when they are most needed

Targeted model evaluations for climate services: a case study on heat waves in Bangladesh

Though not a sufficient condition, the ability to reproduce key elements of climate variability over the historical record should be a minimum requirement for placing any confidence in a model's climate forecasts or projections of climate change. When projections are used to guide practical adaptation, model evaluations should focus on the weather and climate events of interest to decision-makers, their physical drivers in the climate system and their variability on decision-relevant timescales. This paper argues for a greater emphasis on such targeted model evaluations to enable useful climate services. We illustrate this approach through a case study on heat waves in Bangladesh, but draw wider conclusions that are applicable to climate services development more broadly. The simulation of heat waves in Bangladesh is evaluated in several climate models, focusing on timescales relevant to the long-term viability of a heat action plan: the average, interannual variability and seasonality of temperature and heat-wave frequency. Where the physical drivers of variability are broadly captured, a considered interpretation of the models could provide insights into future heat-wave behaviour. However, substantial biases are found in the statistics and in some physical drivers of heat, raising questions about the suitability of some of the models for determining certain aspects of future risk. Specifically, simple bias corrections cannot be used to make inferences about possible future changes in various weather statistics such as timing of heat waves during the year. Results emphasize the potential pitfalls of performing only perfunctory climatological evaluations and highlight areas for model improvement in the simulation of South Asian climate variability

From advocacy to action: projecting the health impacts of climate change

Mitigating climate change by reducing greenhouse gas emissions has many measurable co-benefits for public health and remains a priority (Haines et al., 2009). However, recognition that climate change is already underway has led to an increasing focus on adaptation. Studies projecting the impacts of future climate change on health date back to the late 1980s and their number has grown substantially in recent years. Climate change impact assessments generally use the output of global climate models (GCMs). Here we profile, and suggest means for addressing, challenges associated with the use of GCM projections and impact studies to inform adaptation

Correction to: Climate-proofing a malaria eradication strategy

An amendment to this paper has been published and can be accessed via the original article

Climate information for adaptation: from years to decades

In writing this book, we have prioritized shorter timescales of weather and climate variability because these have most direct relevance to the types of operational and planning decisions made in the health sector. However, in this chapter, we focus on longer-term health decisions that require climate knowledge and information at timescales beyond a year to multiple decades (up to 50 years). This time span incorporates both natural decadal variability and the influences of anthropogenic climate change. It is an extremely challenging timeframe at which to work because of the very limited operational predictive skill at multiannual timescales, and limited confidence about how rainfall may change on longer timescales. Despite these challenges, the timescales under consideration, especially the next five to 20 years, are particularly important when considering how to adapt to climate change. Here we discuss what we do and do not know about the climate in the coming years. We then focus on the practical use of climate information in understanding observed climate impacts on health as well as the prediction of multi-annual to multi-decadal risks. The multi-annual timescale is often referred to as ‘decadal’, even though the forecasts are for periods less than a decade

Defining and predicting heat waves in Bangladesh

This paper proposes a heat-wave definition for Bangladesh that could be used to trigger preparedness measures in a heat early warning system (HEWS) and explores the climate mechanisms associated with heat waves. A HEWS requires a definition of heat waves that is both related to human health outcomes and forecastable. No such definition has been developed for Bangladesh. Using a generalized additive regression model, a heat-wave definition is proposed that requires elevated minimum and maximum daily temperatures over the 95th percentile for 3 consecutive days, confirming the importance of nighttime conditions for health impacts. By this definition, death rates increase by about 20% during heat waves; this result can be used as an argument for public-health interventions to prevent heat-related deaths. Furthermore, predictability of these heat waves exists from weather to seasonal time scales, offering opportunities for a range of preparedness measures. Heat waves are associated with an absence of normal premonsoonal rainfall brought about by anomalously strong low-level westerly winds and weak southerlies, detectable up to approximately 10 days in advance. This circulation pattern occurs over a background of drier-than-normal conditions, with below-average soil moisture and precipitation throughout the heat-wave season from April to June. Low soil moisture increases the odds of heat-wave occurrence for 10–30 days, indicating that subseasonal forecasts of heat-wave risk may be possible by monitoring soil-moisture conditions

Should seasonal rainfall forecasts be used for flood preparedness?

In light of strong encouragement for disaster managers to use climate services for flood preparation, we question whether seasonal rainfall forecasts should indeed be used as indicators of the likelihood of flooding. Here, we investigate the primary drivers of flooding at the seasonal timescale across sub-Saharan Africa. Given the sparsity of hydrological observations, we input bias-corrected reanalysis rainfall into the Global Flood Awareness System to identify seasonal indicators of floodiness. Results demonstrate that in wet climates, even a perfect tercile forecast of seasonal total rainfall would provide little to no indication of the seasonal likelihood of flooding. The number of extreme events within a season shows the highest correlations with floodiness consistently across regions. Otherwise, results vary across climate regimes: floodiness in arid regions in Southern and Eastern Africa shows the strongest correlations with seasonal average soil moisture and seasonal total rainfall. Floodiness in wetter climates of West and Central Africa and Madagascar shows the strongest relationship with measures of the intensity of seasonal rainfall. Measures of rainfall patterns, such as the length of dry spells, are least related to seasonal floodiness across the continent. Ultimately, identifying the drivers of seasonal flooding can be used to improve forecast information for flood preparedness, and avoid misleading decision-makers

Climate impacts on disasters, infectious diseases and nutrition

The Zika virus epidemic that emerged in northeast Brazil in 2015 occurred during an unusually warm and dry year. Both natural climate variability as well as longterm trends were responsible for the extreme temperatures observed1 and these climate conditions are likely to have contributed to the timing and scale of this devastating epidemic. Knowledge of this climate context is derived from analyses of large-scale global climate datasets and models, which provide policy-makers with broad insights into changes in hydro-meteorological extremes. However, societal response to epidemics works at multiple levels. For instance, policies and resource commitments may be developed at international and national levels, while targeted prevention and control efforts are managed at local levels by district health teams and community leaders. Adaptation to climate change also needs to be developed at multiple levels. National level information may be needed for planning, but an understanding of the local weather and climate that individuals and communities experience is also required. Once specific climate-sensitive health risks are identified, information on the past, present or future climate can be used to help mitigate risks and identify new opportunities for improved health outcomes. This information needs to be provided as a routine service if it is to support operational decision-making