Huge Challenges

In this series of post specially dedicated to the role playing environmental modelling facing the global environmental change, we analysed different models approaches and the huge challenges facing modellers. Probably the most significant analysis that should carry out regarding to the weaknesses of models, it is the opportunity that those areas give us to improve models.

The huge environmental threat has placed to models in a really significant role. As the title of this blog say, there are enormous challenges for a huge concern. This huge concern has led to an intense global debate and models are giving us day to day more understanding and more accuracy in the prediction about our future and the potential negatives impacts; the threat is real.

As a result of this massive concern, another challenge arises from the necessity to communicate how the different models work and how the outcomes obtained by complex methods should be suitably communicated to stakeholders. This group involves not only decision makers (authorities) but also the global community. All of us are potential affected. A suitable way in this direction it is to include the technical aspect behind environmental modelling in an accessible and clear system for the community and political authorities. For instance, through simplified and interactive web tools or through open public presentations. In this task, as Hall et al. (2014) have argued, a major difficulty has been that students has been educated in technical aspects of models but the normal tools of education in this field have omitted the role of communication.

When specialists are presenting relevant results to communities, the interpretation of results can be influenced by personal biased concerns which give different point of view from a non-technical perspective. The experience of knowing the concerns (even from personal and partial point of view) of potentially affected communities could be highly beneficial for certain elements in the research. 

Finally, it is fundamental to look the future with a clear understanding of models as an abstraction of reality, with strengths and weaknesses. Although the final result is taken from the model, this is finally given by the capacity of the modeller to interpret those outcomes in a particular modelling context.

Real-Time Floods Monitoring

I found a really interesting article about an innovative flood monitoring system. The Global Flood Monitoring System (GFMS) consists in a real-time modelling tool, which is based on a hydrological model working at real-time with the TRMM Multi-satellite Precipitation Analysis (TMPA).

In the web page of GFMS, it is possible to take a look on the interactive real-time inundation maps (see Figure below) updated every 3 hours and it is also available a modelling tool for 4 - 5 days flood forecasting. I’m inviting you to explore this brilliant idea!!. If you want to going deep on this, you should look at the paper of the developed study and some relevant details about the model validation.

Some specific limitations are related to the spatial cover of the model between the latitudes 50°N and 50°S. Also, the estimation does not consider the topography due to anthropogenic interventions (e.g. mining projects). However, this quasi-global model represents an excellent tool that contributes to predict the harmful effect of this natural phenomenon on communities.

The Global Flood Monitoring System (GFMS)

Floods: Ways to Predict the Threat

During the last time, northern UK has suffering serious floods events in populated areas. Precipitation and other meteorological variables have reached historical values leading to huge problems for community. In this context, using modelling tools in order to understand and predict this natural process it is essential in order to anticipate future damages for population.

Hydrological modelling has been essential in this analysis. In effect, varied model approaches based on hydrological theory, such as fluid dynamics technics, have been used to simulate floods dynamics, its natural origins and also the effect of climate change on these events. 

Floods in Northern UK, 2015

In some places, floods can be strongly related with snow melt dynamics. Therefore, snowmelt models based on hydrological modelling principles have been widely developed in order to predict future disasters. In addition, some economic activities such as forest harvesting have also been recognized as a determining factor which could contribute to negative impacts in the natural runoff patterns. 

An interesting study partly funded by the Provincial Government of Salzburg was carried out in the Alpine basins. According with the nature of floods events in that area, the researchers included the snowmelt dynamics in a novel real-time flood forecasting. In the Alpine, the snow melt has been relevant in runoff processes and flood events. Thus, building and calibrating a model considering suitably this process, it was fundamental to create an efficient flood forecasting tool.

The model, which was calibrated with monitoring data of precipitation and temperature for the period 1999 – 2005, it was applied on the Salzach watershed. Due to the modelled area has a surface of 600 km², this was divided in 10 sub-basins.

According to the outcomes, even though the model shows some limitations in the snow depth and snow cover simulations, the runoff seasonal behaviour was represented with a significant accuracy. The Figure below presents the modelled runoff in comparison with the observed data for 2004 and 2005.

Runoff Estimation: Ovserved v/s Simulated  

This study case shows a significant contribution due to the ability to predict a natural hazard. In cases such as in the UK, where precipitation is playing a fundamental role in these natural events, using hydrological models including local precipitation dynamics represents a relevant tool for risk management. Therefore, a great challenge of environmental modelling facing the climate change, it is not only the understanding and prediction of this hugely complex phenomenon but also to join this effort with other fundamental disciplines for population welfare, such as urban planning and natural risk-hazard management.  

Climate Modelling IV: GCMs Study Case

In parts I to III of previous climate modelling posts, we commented general aspects of climate models, how they work and also the main challenges and difficulties facing modellers.  In this fourth and final part of this series of posts, I want to show you an interesting application of Global Circulation Models (GCMs).

In Asia, the Mekong River (see Figure below), which is the 12th longest river in the world with a length of 4,350 kilometres, represents a fundamental source of resources for different economic activities in Southeast Asia, such as agriculture and fishing. Another fundamental aspect to consider is the strong intervention of the river with large hydraulic projects, such as dams for hydropower generation. Due to the river represents a fundamental source of resources for the different cities and human settlements located in the basin, interesting studies have been carried out in order to assess the potential effects of climate change in the Mekong River.

Mekong River 

Kingstonet al. (2011) explored the potential effects of global environmental change on water resources in the Mekong river basin and they also proposed a method to estimate the uncertainty of this prediction. For this purpose, they integrated GCMs with hydrological modelling. This method represents a really interesting approach to predict potential effect of climate change on the environment; different disciplines working together in order to create more sophisticated tools.

In addition, another significant contribution was to consider different climate scenarios given by a GCMs structure. Basically, they built different GCM scenarios working as a base for a hydrological model (SLURP) of the study area. The fundamental strength of this methodology was to observe modelled responses of the freshwater behaviour due to different climate scenarios. Specifically, they generated different global warming scenarios between 0.5 and 6.0°C. For the 2°C scenario, they used seven different GCMs. Although there were clear similarities among the GCMs, it was useful to use different models structures to analyse the influence of these structures in the final projection uncertainty.

Mekong River 

According to the main purpose of the study, the effort of researchers was mainly focused on the understanding and measurement of the model uncertainty. An interesting result was to find a significant effect on the uncertainty due to the differences in precipitation projections given by the different GCMs, even for the same scenario. As we could see in the previous post, one of the most relevant challenges facing GCMs modellers is to improve precipitation and water cycle simulation. This was also emphasized by other researchers in a study developed with multiple GCMSs in India, where were found severe uncertainties in future rainfall estimations. Conversely, a proper consistency was found in GCMs projection for both snowmelt season and evapotranspiration in the Mekong river basin.

Finally, the most significant outcome was related to the strong dependence of hydrological behaviour in the basin (discharge) with potential changes in precipitation patterns. Due to the seven GCMs applied and considering the different precipitation estimations of these, for the 2°C scenario the parameters of the hydrological model were numerically estimated with an uncertainty between -2.0 – 2.0% and the discharge pattern was suitably represented by the model (see Figures below).

As we can see, the obtained results ratify the proposed methodology of applying different GCMs in order to obtain a suitable estimation of the final uncertainty, minimizing this value and finally obtaining better projections. 

Mekong River, Parameter Uncertainty for HadCM3 (GCM) Model, 2°C Scenario

Mekong River, Monthly Discharge for Base – Line and Seven GCMs Applied, 2°C Scenario

COP 21: Never Reach ......... 1,5°C

Today is a historic day for humanity!.

That is the headline of many newspapers and web pages which have been following the international conference on climate change (COP21) in Paris. Today it was presented the final draft which summarizes two week of negotiations by representatives from 195 countries.

One of the most significant agreements is about the new target for global warming temperature. As we noticed in the previous post, the target of 2°C meant the starting point of the discussion for this version of the conference. Due to scientific evidence given along COP21 sessions, this goal changed: it's essential to pursue a lower value, with special efforts to restrict the increment to 1,5°C. According to categorical simulations this new target represents a significant and favourable change of scenery regarding to the potential effect on the global environment.

Another interesting point is related to finance. The funding of $100 billion per year by developed countries to support environmental policies in developing countries will start operating by 2020. This represents a substantial measurement to promote an integral solution with developing and developed countries working together. A global threat necessarily requires being tackled with a common view.

Although this represent an important advance facing the global warming, it´s necessary to be aware about what 1,5°C means; this figure must be analysed again, and again. Seems to be clear that the effect on climate change should be significantly less and probably some extreme consequences may be avoided by 2100, however, it´s still a 1,5°C increment; which are the environmental effects of this new scenery?. As can we see, environmental models and their continued progress have undoubtedly been (and they will remain being) a major player.

I invite you to take a look to the COP21 web page and read in detail this already called “historic document”. 

 “If it is adopted, this text will mark a historic turning point” (Laurent Fabius, president of COP21)

COP 21: Never Reach 2°C

As many of you may already know, these days from November 30 to December 11 it is holding the international conference on climate change COP21 (21st Conference of Parties) in Paris. Authorities from United Nations are carrying out negotiations in order to define effective measurements to keep the level of global warming bellow 2°C by 2100. But, why this number?, why 2°C?.

Although the debate around this figure is quite complex, the video below offers an interactive explanation for this value defined in 2009 by the International Panel on Climate Change (IPCC) in Copenhagen. The video summarize relevant environmental phenomena identified in the last two centuries such as relationship between sea level and pH, CO2 and global temperature, dynamic of Arctic sea ice, greenhouse anthropogenic emissions and trade-related CO2 transfers.

For the special purpose of this blog, considering the different perspectives that we have been discussing here, it is absolutely interesting to visualize the essential role of environmental models underpinning the analysis and projections of potential temperature and precipitation anomalies by 2100 (and their massive consequences) for each studied scenario. 

Climate Modelling III: Global Climate Models (GCMs), Challenges of Modelling the Whole Planet

In the previous post we reviewed general concepts of climate modelling. In this post the GCMs approach will be analysed, which are mainly characterized by considering the largest possible scale in the environmental science: the entire Earth. As we already analysed in this blog, the discrete control volume or box is the minimum spatial unit to represent numerically the atmosphere over a specific portion of the land. In addition to other sources of error, the size of the box is directly related to the model accuracy (Wainwright and Mulligan, 2013).

Although the uncertainty of GCMs has been measured and defined as substantial (Palmer,2014), the identification of sources of uncertainty along the modelling process represents clear opportunities to improving GMCs. In this context, it is fundamental a suitable understanding of the limitations and challenges facing GMCs modellers. This viewpoint was emphasized by Burroughs (2007) who identified different potential areas to improve GMCs and minimize the uncertainty:

• Clouds: this area involves clouds formation processes, their properties absorbing and reflecting sunlight and better understanding of precipitation dynamics (see Figure below). In addition, the anthropogenic emission of particles, such as sulphur dioxide by fossil fuel consumption, leads to a disturbance in the natural balance of sunlight absorption and reflection processes. Moreover, there is still no clarity about the precise effect of this pollution phenomena on cloudiness (increase or decrease clouds cover). Thus, the main challenge is related to the definition of more accurate parameters in models to represent clouds dynamics at suitable space and time scales (Burroughs, 2007).
• Tropical cyclones: it is necessary an adequate representation of tropical cyclones intensity and their effects in energy fluxes processes. According to Burroughs (2007) the resolution of GMCs is unable to represent suitably this local phenomenon highly relevant to predict global warming.
• Land-surface dynamics: it is necessary a better understanding of the effect of some land variables on climate processes. For instance, weather models with considerable higher resolution have shown that appropriate handling of soil moisture data can lead to more accurate rainfall predictions. Therefore, more accurate description of physical features of the land, such as run off, stored precipitation and its interaction with the atmosphere will lead to a better representation between these and their effect on climate forecast (Burroughs, 2007).
• Winds, waves and water cycle: the representation of water cycle and the interaction between oceans and the atmosphere represent probably the most relevant source of uncertainties in GCMs (Palmer2014). Furthermore, the energy exchange between sea and the atmosphere is also essential for climate dynamics. Therefore, including a more detailed representation of this processes as well as an accurate analysis of physical interaction between winds and waves could represent a substantial improvement for climate models (Burroughs,2007).
• Greenhouse gas emission: the representation of multiple emissions of greenhouse gases (e.g. Carbon Dioxide (CO2), Methane (CH4) and Fluorinated gases (F-gases)) represents a relevant source of uncertainty in climate models, due to the dynamics of human activities and behaviour of these pollutant in the atmosphere.

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Simplified Scheme of Absorption and Reflection of Sunlight (Tour of the Electromagnetic Spectrum, NASA, 2014)

As can we see, the challenges to minimize the uncertainty of this powerful modelling tool could be summarized in two main ideas. Firstly, it is necessary to improve the knowledge in some relevant climate processes and define suitable parameterizations of these in GCMs. This effort implies not only a better understanding of complex chemical and physical processes through monitoring data but also requires an improvement in how monitoring activities should carried out for higher resolution scales. Secondly, as Neelin (2011) argues, a factor that must not be disregarded is the spatial and time scale and its close relationship with computational requirement, which are not necessarily available with current technology (Burroughs, 2007 and Palmer, 2014). 

Finally, it is relevant to highlight that some local climate processes may generate large scale effects, such as convective clouds dynamics (Palmer, 2014) and turbulence (WMO, 2015) which are not able to be represented in a typical GCMs horizontal resolution (around 100 km). In this context, Regional Climate Models (RCMs) are playing a fundamental role to understand local phenomena which GCMs are not able to represent (see Figure below). Indeed, both approaches working together represent a current solution. This and other solutions must be urgently explored due to climate models play an overriding role in the understanding and prediction of global environmental change.  

High Resolution Simulation of Convective Clouds System (Palmer,2014)