ENHanCE Position Paper #2 Climate Modelling

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1 ENHanCE Position Paper #2 Climate Modelling Introduction Climate variability is an important component in determining the incidence of a number of diseases with significant human and animal health impacts. Within the ENHanCE project, several observed and simulated climate datasets will be employed to model and map the risk of key relevant animal and human diseases for the recent past, and to project them into the future based on climate simulations. Other important non-climatic factors also need to be considered in the disease modelling approach. Within this position paper, the different climate datasets that can be readily employed within the ENHanCE project framework to model diseases for both the recent past and the future over Europe were reviewed. As an illustration, climate diagnostics and disease model outputs were highlighted so that attendees to our agency meeting would be able to identify the approaches most useful to them for future planning. Whilst reading the position paper, the agencies were asked to think about the questions below. Are you/your organisation using climate data (i) observations, (ii) weather forecasts and outlooks (days to seasons) or (iii) climate projections (decades to centuries) in any aspect of your operational work, planning or policy direction? Please give/bring examples if so Have you any on-going or past interactions with the climate science community? If so, what is your source of climate information? Considering the numerous climate models available across Europe, do you favour perhaps one or some run by national agencies or do you prefer ENSEMBLE approaches which can present overlap and uncertainty between models? What sort of climate information would be most beneficial to your organisation? What time slices are most important for your agency? What spatial scale of output would be most useful for you? What emission scenario would be most useful for you? Following engagement with stakeholders, we feel within the ENHanCE project that when undertaking modelling of the impacts of climate change using different climate datasets we should be aware that: Agencies are generally using climate data (i) observations, (ii) weather forecasts and outlooks and (iii) climate projections within aspects of their operational work, planning and policy direction. Agencies often already have links with the climate science community, usually utilising national centres for meteorological information.

2 The climate models which agencies favour most depend upon the question in-hand; models with higher resolution are, however, interesting regardless of their source, and local models are best to answer local questions. Many agencies do like ENSEMBLE approaches though, as they provide the best fit for data and can give a gauge of uncertainty within forecasts. Agencies find climate information such as seasonal weather forecasts, temperature, rainfall, humidity, and snow cover at the highest resolution and as timely as possible most useful to their organisation s planning of the impact (magnitude severity, exposure, perception, costbenefit and adaptation potential) of animal diseases/zoonoses. The most important time-slices for agencies involve near and mid-term projections rather than longer term; agencies are most interested in the next years, and up to about The most interesting spatial scale for agencies depends upon the question; it was suggested that a coarse resolution was useful for advocacy, and high resolution was useful for actual programme work, however, finest spatial scales were better if climate was being combined with other parameters such as ecological/habitat variables, e.g. for vector distribution work. Different agencies preferred different emissions scenarios dependent upon what they are investigating: worst-case scenarios are useful for advocacy work but also realistic scenarios are better for programme work. Median scenarios give a reasonable illustration for general questions. ENHanCE Position Paper #2 Climate modelling within the ENHanCE project The first step consists of developing a disease model that can be driven by key climate impact variables, for example rainfall and temperature. The model is then tested and validated against real epidemiological datasets. This model can obviously include other important non-climatic factors, such as livestock densities (host availability). Rainfall and temperature can be estimated from station measurements or derived from satellite products. Gridded climate observation datasets can also be developed based on station measurements, using complex statistical methods. As the station network is sometimes coarse, and as certain areas of the world are poorly covered, it is also possible to use reanalysis products. Such products are a mix of various sources of climate observations and modelling at the global scale, integrated together through complex assimilation methods. Within ENHanCE, different sources of climate observations could be employed (see the climate dataset inventory in the appendix).

3 Figure 1: Upper row: Observed winter (left) and summer (right) temperature trends ( C/decade). Lower row: Observed winter (left) and summer (right) rainfall trends (mm/decade).the trends are computed over the period and they are based on the EOBS dataset at 25km 2 resolution. This highlights the climate change context of the last 40 years. Namely wetter and warmer winters over Northern Europe, warmer and drier winters over Southern Europe. There is a significant observed warming and drying signal over the Mediterranean basin in summer, namely more observed drought conditions. These findings are consistent with the results raised by the Intergovernmental Panel on Climate Change (IPCC). A high resolution (25km 2 ) gridded climate dataset has been developed for Europe based on station measurements (Haylock et al., 2008) within the EC FP6 ENSEMBLES project framework (Van Der Linden et al., 2006). Important climatic impact variables, including rainfall, temperature, minimum and maximum temperatures, are available over Europe for the period at daily temporal resolution (for example Figure 1). This observed climate dataset (EOBS hereafter) will be extensively used in ENHanCE to drive and validate different disease models for the recent period. The next step is then to develop a seamless system that is transparent to the users of how the disease risk outcomes are being produced from the integrated climate model disease system. Once systems can quantify climate and health relationships over verifiable time-scales (weeks to seasons and beyond), they can be extended with confidence to climate scales to provide projections for the first half of this century, thus encompassing the time horizons most relevant for health planning activities. Different modelling stream products are already available. The typical weather and medium range forecasts range from days to weeks, seasons to decadal forecasts (probabilistic estimates), and long-term climate projections, based on either coupled general circulation models (GCM) or regional climate models (RCM). Generally, regional scenarios for climate change impacts assessment require finer spatial scales than those provided by global climate models (GCM with a coarse resolution, about 300x300 km). One methodology consists of using a limited area climate model at higher resolution (typically around km 2 ), which is driven at its boundaries by either observations (control runs) or climate change scenarios from a GCM. The advantage of this approach is in having a more realistic reproduction of the small-scale features including the feedbacks with the topography and the land surface characteristics. However, there are still uncertainties associated with the driving GCMs, and the intrinsic biases of the RCM. The ENSEMBLES European project provides such improved regional climate models, at spatial scales of 50 km 2 and 25 km 2, for both the recent past ( ) and future climate scenarios ( ). Within ENHanCE, we retained models for which the spatial resolution is 25 km 2 and that cover the European domain. The grid spans N to N in latitude and W to E in longitude, with a regular 0.25 step. It is the same resolution as the observed grid, but spans a smaller area. There are two types of simulations. (1) For the control experiments, the limited area model (or RCM) is forced at its boundaries by the ERA40 reanalysis (which serve as a set of initial

4 conditions, calculated from a blend of simulation and station observations, satellite data, ship measurements through complex assimilation mathematical process). Realistic external forcing is also applied to the models (greenhouses gases, solar, volcanic, aerosols). All the included RCMs have been run with the most realistic boundary conditions and external forcing. The experiments, driven by the reanalysis, span a common period from 1961 to (2) For the future climate scenarios, the RCMs are driven at their boundaries by a coarser general circulation model forced by the SRESA1B emission scenario (moderate GHG and aerosols emission scenario, see Nakicenovic et al, 2000). The GCM that has been used to drive the different RCMs can vary from one operational centre to another. For instance, the CNRM i.e. the Météo France research centre uses its own GCM (ARPEGE- OPA) and so did the Met Office for the UK (HadCM3). This provides a better estimate of the related uncertainties, namely the climate change envelope. The selected common time period spans RCMs from different centres have been retained (see appendix). Further details about the regional climate models that will be employed in ENHanCE are available at Figure 2: Upper row: Simulated winter (left) and summer temperature mean future changes ( C). Lower row: Simulated winter (left) and summer rainfall mean future changes (mm). These differences are computed between the time slices and The multi-model ensemble mean is depicted in shading and the sign consistency across the different models (80% of the models agree on an increase or a decrease) is shown by the black dotted area. All RCMs simulate a warming, faster over northern Europe in winter and southern Europe in summer. The winters get wetter over northern Europe for both seasons and there is a consistent drying signal over the Mediterranean basin. This is consistent with former findings highlighted in the last assessment report of the IPCC. For example, Figure 2 shows the simulated mean future changes for rainfall and temperature over Europe. This is carried out for the average of the 12 regional climate models; the sign consistency across the different RCMs is then depicted by the black dots. These simulated future changes are relatively similar to the climate trend observed over the last 40 years (figure 1). These climate change scenarios must be considered with caution, as there are still biases and uncertainties associated with the actual state-of-the-art climate models and the emission scenarios. First of all, the emission scenarios rely on different demographic and socio-economic projections that cannot be verified. As all emission scenarios are relatively similar to each other until the 2040s, the differences across different emission scenarios as simulated by a single given climate model will be small. The spread in projection will then increase afterwards (Solomon et al., 2007). In ENSEMBLES, the SRESA1B scenario has been retained as it seems to be the most realistic projection with respect to what has been observed over the last 10 years (Solomon et al., 2007). A range of users including humanitarian and development policy makers want to develop and implement adaptation strategies for the next decades (particularly the next 10 or 20 years). At this time-scale, the selected emission scenario does not really matter, but the simulated climate change signal is

5 relatively small compared to its 2070s projection. Secondly, there are still biases associated with climate modelling. Each climate model can have a different sensitivity to the expected rise in atmospheric GHG concentrations, and they can poorly represent key observed climatic features. Within ENSEMBLES, the strategy consisted of producing a large set of climate simulations based on the actual state of the art European regional climate models (see appendix). This allowed estimation of the multi-model ensemble consistency; both the magnitude and the sign of the simulated climate change signal. As the model ensemble is large, this is a major advantage with respect to the UKCIP project (see appendix). There is no deterministic climate forecast of the future and we need to estimated and bound the climate change envelope (that considers all these uncertainties) to provide the best probabilistic estimate of the upcoming health impacts for the future. Within the ENHanCE project, different methods will also be developed to correct bias in the simulated climate, with respect to the observations, to assess with greater confidence future climate scenarios. Impact of climate upon vector born diseases: two application examples Key climatic variables have been used to model and map two vector-borne diseases over Europe: bluetongue (BT) and malaria. The BT R 0 model (a powerful tool to assess the level of risk posed by a specific disease) has been developed within the ENHanCE project framework. This model is driven by both the EOBS climate observations and the RCMs climate scenarios for the recent climate and future climate projections. It considers both virus and vector dependence upon climatic drivers, and includes an estimate of sheep and cattle host densities. Figure 3 depicts the BT R 0 climatology (late summer-early autumn) which relies on observed climatic drivers. Such analysis of idealized experiments highlights realistic R 0 features with observed outbreak evolution. Figure 3: Simulated mean BT risk over western Europe based on the EOBS high resolution observed climate dataset for the season August-September-October over the period The BT risk (derived from R 0, the basic reproduction number) is arbitrary scaled to range between 0 and 1. Consistent with epidemiological field measurements, the mean BT risk is high over Spain, Portugal, south western France, Sardegna and Sicilia, but misses out observed outbreaks in Corsica. There are, however, unrealistic values over Eastern Europe and over the mountains. The mechanistic model highlights that changes in BT risk over southern Europe are mainly related to the spread of the Afro-tropical vector whereas changes in the pathogen properties mainly drive the simulated BT changes over northern Europe. The future scenario projects higher risk over northern Europe, mainly related to the temperature increase which impacts on the BT virus properties (not shown, see Guis et al, 2010 for further details). For interest and partly to deflect questions about when climate change is likely to lead to malaria in Europe, we have run a Liverpool Malaria Model (Hoshen and Morse, 2004) using different sets of observed gridded data of the current climate to plot potential malaria risk maps (Figure 4). The model suggests that some areas are climatically at risk for the recent period. This is a preliminary result and we have not diagnosed what aspect of the climate data is leading to differences across

6 the sites. The next step is to drive forward the Liverpool malaria model with the RCM climate projections to highlight potential changes for the future. Figure 4: Mean simulated malaria incidence (%) based on different climate observation datasets for the season July- August-September over the period Malaria incidence is derived from ERAINTERIM and NCEP reanalysis and the EOBS high resolution dataset. The malaria model was run with its normal African setting bar a change in the sporogonic threshold from 18 C to 16 C to reflect more likelihood of having vivax rather than falciparum infections in Europe. All simulations highlight that the Balkans, northern Italy, some parts of Galicia in Spain and the Landes region in France are climatically at risk, even if the values are relatively low in magnitude (20-50%) compared to what can be expected in Africa. In summary, several observed and simulated climate datasets will be employed to map key diseases over Europe for both the recent past and to project their risk into the future. We have already demonstrated the feasibility of such an approach for bluetongue and malaria over Europe. Other models and methods will be developed within the ENHanCE project framework to improve our understanding of the potential impact of climate change upon the disease burdens in Europe. References Guis, H., Caminade, C., Calvete, C., Morse, A., Tran, A. & Baylis, M. (2010) Integration of climate and disease models gives mechanistic insight into vector-borne disease emergence, submitted to PNAS. Haylock, M.R., Hofstra, N., Klein Tank, A.M.G., Klok, E.J., Jones, P.D. & New, M. (2008). J Geophys Res 113(D20119). Hoshen, M. B., & A. P. Morse (2004). Malaria J., 3, 32, doi: / Nakicenovic N & Swart R eds (2000) Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change (Cambridge University Press, Cambridge), Vol 1, p 599. Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M. & Miller, H.L. (2007) Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp. P.J. Van der Linden & Mitchell JFB (eds). 2009: ENSEMBLES: Climate change and its impacts: Summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, Exeter-UK. Appendix Climate dataset inventory stored at the school of environmental science, University of Liverpool: UKCIP: UK Climate Impacts Programme available at: Here is a list of the Regional Climate Models and the related climate centres: C4IRCA3 (MettEire, Ireland), CNRM-RM4.5 (CNRM, Meteo-France), DMI-HIRAM5 (DMI, Denmark), ETHZ-CLM (ETHZ, Switzerland), ICTP-RegCM3 (ICTP, Italy), KNMI-RACMO2 (KNMI, Netherlands), METNOHIRAM (MET.NO, POLAND) METO- HC(Met-Office, UK), MPI-M-REMO (MPI, Germany), OURANOSMRCC (OURANOS, Canada), SMHIRCA (SMHI, Sweden), UCLM-PROMES (UCLM, Spain).

7 Climate Modelling Position paper #2 Questions Is your organisation using climate data (i) observations, (ii) weather forecasts and outlooks or (iii) climate projections in any aspect of your operational work, planning or policy direction? (i) observations, (ii) weather forecasts, and (iii) climate projections 45% None 25% Have you any ongoing or past interactions with the climate science community? If so, what is your source of climate information? Yes 55% No 27% Considering the numerous climate models available across Europe, do you favour one or some run by national agencies or do you prefer ENSEMBLE approaches? Either, depending on scenario 25% No opinion 55% What sort of climate information would be most beneficial to your organisation? Not sure 34% Temperature, rainfall, humidity, measures of seasonality What time slices are most important for your agency? Medium term % Depends on the scenario What spatial scale of output would be most useful for you? Dependent upon the use Highest resolution available, e.g. regional/sub-national 54% What emission scenario would be most useful for you? Not Sure 46% Medium to high scenarios (A1/B2) Answers (i) observations, (ii) weather forecasts (iii) climate (i) observations,, (ii) weather forecasts, and (iii) climate projections Limited ENSEMBLES National Agencies Those that have major impact on animal diseases/zoonoses Seasonal weather forecasts, long-term predictions of climate change at high resolution As much and as highly resolved as possible; daily (or weekly average) temperature, rainfall etc. The identification of climate parameters which impact infectious diseases Short term 5-10 years Long term Not sure Low to medium scenarios (A2/B1) Country-level resolution Full range of scenarios

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