Climate Models and Predictability

A NOAA data buoy in the Atlantic Ocean east of Boston, MA

            One of the greatest problems with adjusting to climate change is the uncertainty in climate models. Climate scientists use models to demonstrate the statistical likelihood of certain futures in a certain region. They have evolved over time to include more data which has made them more accurate. Climate models can be hugely helpful in making decisions for the future because they can produce a range of possibilities for the future based on the factors they include.

They are especially helpful when the majority of models predict a small range of possibilities that can inform mitigation efforts. In order to best prepare for the future we need to have some idea of what it might look like. Policy makers and organizations can use these models to prepare for the futures that are most likely. For example, if a model predicts that a certain area will see an increase in annual rainfall, infrastructure can be changed to accommodate that.

However these models can vary in their accuracy. As models get more specific and focus on smaller areas, the information they provide can become harder to use because they can produce a wide variety of possible futures. This is also a problem because climate change take uncertainty in models as reason to doubt the basic facts of climate change. This is also difficult for decision making because predictions can vary widely and models can disagree. For example, when climate models focus in on Nepal they can vary hugely in their predictions. There are several reasons for this, especially in Nepal. The region is very unique because of the huge variation in elevation across the country so already the weather across the country varies greatly. Additionally, Nepal has a dry season and a wet season when most of the rain falls in a short amount of time. Yearly rain predictions don’t help much to say when the rain might occur and where within the country. The climate models predict anything from a lot more rain to more droughts and a large range of possible temperature increases. This complicates decisions that can be made because it is hard to know which future to prepare for.

Mitigation efforts are going to be expensive and in some cases traumatizing, especially when people decide to migrate, people aren’t going to want to invest in changes if they aren’t shown sufficient evidence. Not only might politicians and organizations make ill informed decisions but they might not make any decisions at all. Additionally, weaker evidence for a certain future will provide more challenges for people trying to implement a certain plan. For example, politicians in the United States who are trying to implement climate change mitigation strategies are constantly met with opponents who say that there simply isn’t enough evidence to warrant spending huge sums of money on mitigation. This goes to show that while climate models can be very helpful, they can also have their shortcomings. It is important to keep in mind that being less helpful in certain regions does not make them illegitimate sources of information in others and that they should be analyzed within the context of the geography of the regions they are describing.

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Climate Models and Decision Making

Scientists have used climate models to project climate conditions in the future under various scenarios. Looking at the movement of energy and materials through the climate system, climate models are referred to as general circulation models (GCMs) (NOAA). In order to simulate the climate system, GCMs use mathematical equation of physical processes in combination to calculate each grid cell. However, this form of analyzation has limitations.

Since models are a simplified view of reality, climate models do pose constraints for decision making. Models only account for some and not all factors. There can only be a certain resolution depending on the scope of a model. Looking at different “time steps” like minutes, hours, days, or years could change the precision of the results (NOAA). For example, by using higher resolution and smaller grid cells regional climate models can give you more precise information than GCMs. The information nonetheless can still vary dramatically depending on the parent GCM used to calculate. Due to the time constrains for relevant information, larger time steps may be used in order to gain more immediate results because they can be computed much faster. Instead of more detail results, climate models are streamlined in order to run multiple scenarios. Climate models do not portray actual conditions. Since models are derived from using physics equations, changing input conditions can impact results.

Depending on initial parameters for example the GCM used for regional climate modeling, different results could be reached.  Even when studying one scenario, A1B, and both using the PRECIS Regional Climate Models, changing inputs to the model changes results can vary results. Using different parent GCMs, Had-CM3 and ECHAM5, results radically changed. The Had-CM3 results indicated that the river flows would increase from the baseline in dry and very dry periods in the earlier dry season months, November to January, and a major decrease in the latter part of the dry season, April to May, especially during the 2031-2040 period (IDS-Nepal 97). ECHAM5, on the other hand, projected that there would be a decrease in flows from the baseline in the dry and very dry periods in the early months of the dry season but then instead a greater decrease in river flow in the latter part, increasing the dry season problems.

These models are important because they study the future impacts of climate change on the hydro-electricity availability which is important for the electricity sector. By looking at different hydro condition scenarios, we can better judge the future conditions. Because the Had-CM3 data shows an increase in river run off during dry month with climate change, hydroelectric power plants would be able to function lowering the total cost of power generation. ECHAM5 proposed the opposite findings. The capacity mix of electricity of hydro, thermal, and import would have to shift as a result increasing cost to US$ 2,580 million above the baseline over the present value of total investment to 2050 (104). This scenario suggests greater investments in storage plants and thermal plants.

This type of climate modeling can be useful for understanding the range of effects climate change would have in the future. We can test theories, by using multiple scenarios and GCMs, and comparing the results. Overall better-informed decisions can be made by considering all risks. Taking a holistic adaptation response to risk analysis is important (86). Aspects like allocation of assets for investment can be informed and comprehensive. Looking at projections for the future, communities can focus on resilience instead of stability. Even though there are significant uncertainties about the future climate, decision can be made to respond to climate change. Climate models can show us a range of outcomes, and through focusing on resilience we can prepare to adapt or mitigate climate condition impacts by acting. Preemptive action and decision making can decrease future costs of impact and increase human security. In conclusion, climate models are important for the decision making process because it allows you to see multiple scenarios and the effects of changing inputs and make better-informed decisions.

(When looking at the results for the red and purple, Had-CM3 as more extreme result for the latter months of April to May.)

Works Cited

National Ocean and Atmospheric Administration. “Climate Models.”, National Ocean and Atmospheric Administration,

IDS-Nepal, PAC and GCAP (2014). Economic Impact Assessment of Climate Change In Key Sectors in Nepal. IDS-Nepal, Kathmandu, Nepal.

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A Precautionary Principle for Climate Models and Decision-making

Definitions of risk are often framed around the possibility of harm. Decision-makers will never have 100% certainty, especially when it comes to climate variability. The UNFCCC does not allow incomplete data to limit global response. Instead, it follows a “precautionary principle” to adapt/mitigate climate change even when scientific certainty has not been reached. By their very nature, climate models simplify complex systems. Decision makers should respond to long term predicted changes in climate. Even if the models’ predictions do not come true, the actors have improved their resilience for future change.

From WikiCommons

Climate models in Nepal are imprecise about the magnitude of change but they are useful to understand variability. Uncertainty is even more of a reason to respond to climate change because many sectors – agriculture, trade, energy – rely on some level of predictability. If communities can anticipate variability, they may respond either reactively or resiliently. A reactive response plan would dynamically respond to any climate event depicted in the model. A resilient plan would build general structures to reduce the impact of any climate event – diversifying income sources, saving, or even moving. Without specific magnitudes, the stakeholders may not plan for specific events such as higher rainfall or a drought. A generalist approach to resilience based on uncertainty could be more useful to respond to more than just the coming crisis.

In the short term (2030), productivity and yields for some staple crops (maize, rice) may increase across Nepal. It is the responsibility of policy-makers to guide subsistence driven farmers and other actors toward the long term view – these crops will decline in productivity by 2070. These long-term decisions are difficult private actors that depend on individual harvests or bottom line profits. Policy-makers should institutionalize climate adaptation strategies even if climate models show uncertainty because uncertainty is still a threat to people (Zambrano-Barragán). Similarly, they should be able to critically analyze probability or hire personnel to do so.

This image requires alt text, but the alt text is currently blank. Either add alt text or mark the image as decorative. From IDS Nepal

Autonomous adaptation from people living and working in areas impacted by climate change should be facilitated by climate data. Policy makers can build institutional supports to encourage bottom-up adaptation with climate insurance systems, technical assistance, or infrastructure investment. These resilient systems are generalist in approach. They would mitigate the impact of any climate shock, regardless of certainty in the climate models.

Policy-makers and community actors should develop robust climate responses even if models are uncertain. If climate outcomes are less severe than predicted, the community will have bolstered its resilience for other, perhaps non-climate related shocks. If climate events are more extreme than anticipated, resilience systems will be better equipped. Climate responses are broadly sustainable. Conventional social support structures like insurance or infrastructure do not pretend to come from exact predictions of future outcomes. Neither should climate policy.

From Climate Action Reserve


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Making decisions in the light of uncertainty from global climate models


Models are important in all sciences. They are created to synthesize knowledge and quantify effects through mathematical equations. Global climate models are created based on the fundamental laws of physics, as they attempt to translate natural cycles of the earth into mathematical models that are able to depict a generalized image of future changes. The extent of accuracy of these models, however, are still in question. As global climate models grossly generalize natural cycles and fail to draw connections among the many existing relationships between natural processes, different climate models with different assumptions generate varying results, sometimes these results are even contradictory. Another limitation of climate models is that they are unable to generate reliable results on a small scale. While the use of local meteorological data to create regional climate models is possible, downscaling climate models does not increase the level of accuracy of these depictions. For the case of Nepal, modeling is particularly difficult due to the complex topography of the country, with elevations ranging from 0 – 8000 meters within an extremely small area. Given the inability to model topographical changes in such an area, GCMs generate results with high uncertainty for Nepal.


Figure 1. The uncertainty in designing adaptation responses using climate models





Although climate models are constantly being improved by scientists, including more intricate natural processes, along with increasing levels of computational power, the results that they generate will not provide with a high enough degree of confidence to allow precise adaptation decision-making. As we have seen in the IDS Nepal report, predictions for precipitation change range from -30% to +100% in annual rainfall, and temperature increases range from +2 C to +6 C based on different emissions scenarios. Does this mean that climate models are should not be incorporated into any decision-making processes relating to mitigation and adaptation responses due to its high level of uncertainty? Absolutely not. While small-scale and short-term predictions have high levels of uncertainty, climate models are able to predict a general view of the long-run. Given such uncertainty about the future, the approach of policymakers should not be focused on specific adaptation plans for a specific climate scenario, but more of a risk-prevention approach for a wide range of scenarios that may happen. As the cost of prevention is always lower than the cost of addressing the problem once it has happened, the risk minimization approach will help policymakers design effective generalized mitigation plans that decrease the vulnerability of communities to climate change. These policies or projects should focus on increasing the adaptive capacity of communities, as well as their livelihoods, to make them less insecure in the light of possible shocks induced by climate change. For example, while the degree of sea-level rise is still uncertain, the predictions generated by GCMs have shown that sea levels will rise. Instead of debating on whether the science of these models is sound, policymakers should focus on increasing the resiliency of coastal communities by creating buffer zones between the shore and residential areas. As GCMs predict the general trend that the Indian Monsoon is changing in the future, resulting in more intense rainfall and longer dry spells in certain areas of Nepal, policy design should focus on increasing human security, especially food and water security, of communities susceptible to climate shocks. Thus, climate models should not be taken with a grain of salt, but they can be one of the many tools used to inform policy-makers on designing effective adaptation strategies. Given such uncertainty about the future, a generalized risk-prevention approach, where policies are implemented to increase the adaptability of vulnerable communities for a wide-range of conditions, should be considered.


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Predicting the Future of our Climate


While many uncertainties still linger in regards to the specifics of climate change, some basic facts are definite. The most sure and important fact is that the climate is shifting rapidly, due to anthropogenic causes. As humans continue to burn fossil fuels and emit greenhouse gases, the overall global climate alters. This, in turn, creates environmental issues, such as flooding, droughts, heat waves, and extreme storms. These events will then threaten individual human well being.

Major decisions are needed to be made in order to respond to the threat of climate change and ensure human safety. However, these decisions can be difficult, due to the uncertain nature of exactly how and when the climate is going to change. Many of these uncertainties are mitigated by using general circulation models, or global climate models (GCMs). GCMs use predicted emissions scenarios to make projections of likely temperature, precipitation and other meteorological element changes over time. These projections can then be used to inform analyses regarding needed adaptations and changes. Responses can be tailored based on the predicted amount and nature of the climatic change in the specific area explored.

Using GCMs, we have an idea of how the climate of Nepal will change over the next century. It is certain that the overall temperature will increase by between 2 and 6 degrees celsius. This temperature increase will then result in more extreme hot days and more heat waves throughout the country. While there is are broadly similar projections between models, space, and seasons, the precise change varies based on emissions scenarios (IDS-Nepal 56). Despite the question in exact amount, the certainty behind the increase allows the government to make response decisions to insure safety and security throughout the country. These extreme heat waves will threaten individual health safety, along with agricultural security.

Alterations in precipitation are far less certain than those for temperature increase. According to the report, there are very dissimilar findings between models, regions, and seasons. Some predictions estimate a decrease by 30 percent of annual rainfall, while others found an increase of over 100 percent. It is likely that there will be an increase in rainfall during monsoon seasons, along a greater frequency of heavier rain days throughout the year (IDS-Nepal63).

While GCMs have grown far more accurate over the past several decades, they still have some limitations. Forexample, due to the drastic variations in elevation and climate in Nepal, it is difficult to accurately predict how the climate will change in precise locations throughout the country. Most climate models work at a grid resolution between 150 km and 300 km, but elevation shifts by over 8000 meters in less than 250 km between the Terai and the high mountains (IDS- Nepal 57). Also, future depends heavily on exact amounts of greenhouse gases emitted, which is still uncertain. In addition, it takes months to calculate these models, and a small variation in original inputted data can shift the entire outcome. While these inaccuracies may be frustrating, climate models are still essential for making decisions and showing likely trends for the future, even if there are some variations. GDMs have become an essential tool for defense against probable climate changes, despite their limitations.

Source: IDS-Nepal, PAC and GCAP. Economic Impact Assessment of Climate Change in Key Sectors in Nepal. Kathmandu: IDS-Nepal, 2014.

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Preparing for the Unknown


A Nepali mountain farmer at work. Neplal’s agricultural secotr is particularly vulnerable to varying weather patterns caused by climate change.
Photo Credit: Bob Webster.

We have good reason to believe that the climate we have come to know is changing. The IPCC reports that “Warming of the climate system is unequivocal, and since the 1950s, many of the changes are unprecedented over decades to millennia.The atmosphere and ocean have warmed, the amounts of snow and ice have diminished, sea level has risen, and the concentrations of greenhouse gases have increased” (2013). Human society is facing conditions it has never seen before, and as these changes continue, our ways of life that have worked in the past may no longer hold up. We will need to meet new problems with new solutions.
For some activities, the relationship with the climate is already fragile, and continuing changes threaten to make the situation even more difficult. The agricultural situation in Nepal provides a good example. IDS-Nepal reports that the sector relies heavily on seasonal rainfall, and variations can have heavy impacts. In the past, “floods, droughts and erratic rainfall” have all impacted the industry, and productivity has been closely linked with seasonal weather and climate (6). Too much rain can flood fields; too little prevents crops from growing; rainfall too late in the growing season can damage the harvest. In fact, changing weather cycles are already causing hardship for Nepali farmers (Reck). If climate change completely upends seasonal patterns, farmers may not be able to grow crops using current methods, and they will have to develop new strategies. The sooner people can adapt, the fewer damages they are likely to face, and the same goes for any change we make in response to climate change.
Unfortunately, it is difficult to predict exactly what conditions we need to prepare for. We can use computer generated climate models to make educated guesses, but they are not accurate or precise enough to inform a highly specialized plan for adaptation.The calculations rely on assumptions about greenhouse gas concentration levels, which may change as countries make or break commitments to reduce emissions. Predictions vary based on which emissions scenario is assumed and which model is used. Even projections of average temperature may contradict one another, and predictions of precipitation vary even more widely (57). Therefore, we cannot say with confidence that any one event will actually happen, and specific preparation for that event would not be very useful. However, these predictions taken together can still shed light on the possible impact of future events, which can inform policy decisions (86). A general idea of the challenges we might face is better than none at all.
By cross checking a variety of models, IDS-Nepal has developed a comprehensive report on the possible regional consequences for Nepal. All of the regional models dependably show a temperature increase (55). Although the models are much less consistent on precipitation, many warn of a rainier monsoon season (63). They also project that floods are likely to become more severe and more frequent (113). The results vary widely by model and by region, creating a lot of uncertainty. However, IDS-Nepal urges that people take these uncertainties into account while still preparing to adapt.Rather than indicating that preparation is hopeless, the wide range of possible changes indicates a need to increase flexibility. By developing multiple adaptation strategies, people can have an array of options to choose from so all is not lost when one falls through. For example, farmers in Nepal might benefit from growing a wide variety of crops that have different growing cycles or thrive in different conditions. Increased use of irrigation would provide more control over how much water their crops receive, so farmers would not have to rely so heavily on natural rain patterns. Government actions might include financing these changes, building infrastructure like dams and levees to keep rising rivers at bay, and setting up relief programs to respond to future disasters. Of course, all of these adaptations would draw on limited resources, and it would be impossible to specifically prepare for every possible scenario. Setting a broad foundation, however, could ease the transition to future adaptations. As models become more accurate and the situation in the real world unfolds, responses can become more targeted. In the meantime, the current models’ predictions provide a good starting point.



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Climate Models: Are They Useful?

Uncertainty Does Not Mean Inaction

Sound decisions can be made about responding to climate change despite significant uncertainties about future climate because high natural variability emerges during the process of modeling Nepal’s climate. It is important to note that Nepal’s climate ranges from subtropical to arctic across the country due to such huge elevation differences across the country. Large variation in local topography and unclear trends in historic annual total rainfall data contribute to this reoccurring and expected level of variability. The need for downscaled regional modeling creates challenges for climate modelers by demanding more precision which general circulation models (or global climate models) lack. Historic data on rainfall patterns do not indicate a clear trend in annual precipitation levels in data from the past 50 years (IDS-Nepal et al. 57).  Monsoon season is just one example of a continuing climate threat to people’s livelihoods calls for immediate risk reduction despite growing uncertainty in how they will change conditions over the long-term. Even when models do not agree on the disruptive behaviors of climate change, natural or cyclical changes in temperature and precipitation remain threats to human security in Nepal because of compounding issues such as widespread poverty and political instability.

A study, by the Integrated Development Society Nepal, consisting of 15 models for monthly rainfall in Kathmandu for the 2040-2060 time period, (at a given emissions level) shows that all of the models agree on increasing rainfall totals in particular months. Compared to the current monthly rainfall totals, all models considered conclude that May, July, and October will have more total monthly rainfall in the future. The July projection shows the greatest potential change in total rainfall ranging anywhere from a 20-45mm increase (IDS-Nepal et al. 59).

Practical Action’s project to create early warning systems demonstrates how rainfall predictions can inform decisions in Nepal. To reduce risk in communities along rivers and in other flood prone areas, Practical Action equips people with the knowledge and the resources to take care of themselves and protect their community against future disasters.

Why Resilience Works

Not only is it less costly to take preventative action, but it is also easier to reduce the risk factors that threaten human security early on rather than intervening later (UNDP 22). A high degree of uncertainty in climate models warrants resilient responses to climate change that will strengthen people’s capacity to cope with a variety of disruptive scenarios. Moreover, resiliency catalyzes community empowerment which build’s people’s capacity to alleviate stress factors within their communities, ultimately helping reduce vulnerabilities from all types of insecurities.

Limitations of Climate Models

One limitation of using climate models for decision-making is that comparing data sets requires matching time periods, levels of data resolutions, and bias corrections. Comparing data sets representing multiple scenarios and model types strengthens analysis and is necessary to account for the range of uncertainty in climate models. Coordinating data collection and preparation procedures for climate modeling requires cooperation at the local, regional, and state levels to ensure that adequate data for downscaled modeling is provided. Increased coordination of precise data collection in Nepal is needed for future studies to cover a wider range of scenarios while capturing large degrees of uncertainty (IDS-Nepal et al. 60-61).

Another limitation of using climate models for decision-making is that monsoons and glaciers add additional layers of uncertainty to future climates. Existing models disagree on potential changes in monsoon season and rainfall at the regional level. Even though an abrupt change in the monsoon could be a tipping point (meaning that it can push the climate system out of balance, causing a larger scale change to the climate system), climate models cannot predict the probability of these large-scale changes. Additionally, temperature increases in Nepal can cause warming which triggers glacial melt water flows. As the amount of ice diminishes, river flows could reduce, but a high degree of uncertainty exists in predicting these changes (IDS-Nepal et al. 66).

Works Cited

IDS-Nepal, PAC and GCAP. Economic Impact Assessment of Climate Change in Key Sectors in Nepal. Kathmandu: IDS-Nepal, 2014.

UN Development Programme (UNDP). Human Development Report. New York: Oxford University Press, 1994.

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The Validity of Climate Models in Predicting Climate Change

The increasing political debate on climate change is unnerving. The science behind the linkage between our earth’s changing climate and human impact has been proven for years. The Intergovernmental Panel on Climate Change (IPCC) has continued to increase their certainty on this cause-effect relationship stating in their fifth assessment report in 2013 that it was “extremely likely human influence had been the dominant cause of the observed warming since the mid 20th century” (IPCC 2013). Climate models, which replicate qualities of our biosphere and the ecosystems of specific locations to predict how the climate will respond in the future. While these models cannot give us 100% certainty of what will happen to our climate in the future, they can give us a range of possible outcomes.

Upon a simple google search of climate models, websites  on “skeptical science” are some of the most frequent results. This could be due to the politicization of climate change, as right wing conservatives- in the United States- continue to deny climate change as a real threat to our nation. Their propaganda is not based on science but rather skewing facts. Some people attempt to discredit the validity of climate models often due to a misunderstanding in the difference between climate and weather. A snowball in a winter season does not disprove the fact that overall global temperatures have been increasing.

While meteorologists face extreme difficulty predicting how much precipitation is going to fall in a certain area on a certain day, predictions for climate are much more broad. Climate patterns stretch across larger geographic areas and are more consistent over time, without human impact. Another important aspect of climate models, that attests to their validity, is that the systems are run multiple times and then the results are analyzed using the median as the basis for what the expected climate will be. This produces a trend that climate scientists can put some trust in.

Climate models are becoming increasingly useful in informing policy responses to the impacts of climate change, showing what changes in emissions could help decrease temperature increases globally and in location specific climate models. Regional climate models show an increase in warming overall in Nepal, but the models for precipitation show much more variation. A regional analysis of precipitation is much more helpful, especially for the people living in Nepal, who are dependent on monsoon season rains that vary greatly among the five different regions. The data that shows these precipitation changes can help inform farmers and others of what kind of variability they might expect in the long run, but it won’t be able to help them day to day, or likely even month to month.

One limitation of global climate models is that distance they account for, in some instances, is too large to account for extreme variance in geographic features. For instance, the drastic variance in elevation in Nepal gets overlooked in the GCMs as the overall area of Nepal is relatively small. Elevation changes by 8000m within 250km in Nepal, because of this GCMs that explain temperature increases, or changes in rainfall do not accurately represent what will happen to Nepal’s climate (IDS-Nepal 57).

Climate models are important for assessing climate impact as they can urge policy makers to consider the impacts of climate warming. One benefit of these models is the ways in which they are able to alter these systems to show what would happen with increasing emissions or decreasing emissions. Comparing the final models can help demonstrate just how important climate action is.


Goverment of Nepal, and Ministry of Science, Technology and Environment. “Climate Model Data and Projections for Nepal.” Economic Impact Assessment of Climate Change in Key Sectors in Nepal, Integrated Development Society- Nepal, 2014, pp. 55–69.

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The Importance of Climate Models

Erik Nielsen

Professor Leary

INTD 250


The Importance of Climate Models

While there are certain uncertainties about the earth’s future climate, I still believe that climate models are very useful for making informed decisions to deal with climate change. To recall what Professor Leary had said during our most recent class,” climate is what you can expect, weather is what you get.” Climate models are simply predictions of weather patterns that we can expect in the coming future given the current trends of greenhouse gas emissions and other similar anthropogenically caused climate factors.

Climate models are postulates that try to anticipate future climactic outcomes. However, often times many climate models are incredibly complex and have to take in mass amounts of data sets which can sometimes lead to errors. While there may be faults involved in these climate models, the overall trends / predictions they make are still quite significant and accurate. Another example from class of an informed conjecture is the statement,” it is hotter during the day than at night.” While I cannot tell you the exact temperature, I can make a knowledgeable inference based on the previous information and trends I am aware of.

Climate models are very similar to this. ‘While we will not know the percent change in rainfall, we can know that there will be a change in the amount of rain.’ Having knowledge of this trend is still very important when it comes to making decisions in regards to adapting to climate change. Additionally, with every new advance in the speed of computers and the efficiency / complexity of technologies, these climate models become more and more sophisticated and precise. Climate models produced by the Intergovernmental Panel on Climate Change are produced roughly a little less than every decade. With each release of a new climate model, the data used and the subsequent models produced have been more robust and accurate with their predictions.

And while there is still uncertainty in the exact future outcomes of the earth’s climate, an overwhelming majority of scientists would agree with the fact that humans are drastically changing the earth’s climate for the worse. “(We) have to recognize that we already have a good scientific understanding of how human-induced environmental changes influence the biophysical environment… Focusing on scientific uncertainty diverts attention away from the factors that generate vulnerability and create human insecurities” (O’Brien 1,2). Using the information from climate models as a foundation to make informed climate decisions will be very necessary in the coming future as climate change worsens. And while we ‘cannot predict the amount it will rain, we know it will.’ So, to ignore climate models due to their partial uncertainties would be misuse of their potential and an injustice towards vulnerable populations around the world. The trends and inclinations produced from climate models are necessary to form the foundation to make the informed decisions that humanity needs to prepare for and adapt to climate change.


Work Cited

O’Brien, Karen. “Are we missing the point? Global environmental change as an issue of human security.” (2006): 1-3.

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Climate Models

One of the key critiques of climate models is that they are incredibly vague. For example the 2100 precipitation change compared to the 1970-1999 baseline in Nepal is modeled to be between -30% and 100%. It is true that no certainty can be gleaned from this result, however when we are presented with a vague result such as this one it does not mean that we should not act. This result essentially means that a range of eventualities could happen in regards to future climate, all we know is that it will be different from present conditions. Not understanding the exact details of future climate is not an excuse for inaction. If actors wait until certainties were reached about every detail of future climate to act it will be too late.
It is important to acknowledge that although climate models may not be able to tell us exactly what the impact of climate change will be on rainfall in Nepal in 100 years there are certain aspects of the climate system that can be forecasted. For example the models have reached a more concrete result in regards to the 2100 temperature change in Nepal. Temperatures will rise between two and six degrees celcius. The difference in temperature rise can be attributed to different emission scenarios. We know that the temperature in Nepal will rise, moreover it will rise at a rate greater than worldwide warming. In multi model projections there are certain areas in which most models agree about the outcomes. These outcomes are not generated by indigestible formulas, but rather by fundamental physics. We know the climate is warming and we know it will continue to warm. The question remains as to how much warming we will experience. Climate models do produce some certainties.
When addressing climate models in Nepal one may consider the work done by the Integrated Development Society Nepal. A study done on agriculture and the impacts of future climate change illuminated food security issues in Nepal. The study acknowledges the uncertainties and limits of their findings, stating that a more comprehensive study of agriculture in Nepal would prove useful. Their findings specifically concerning rice, maize and wheat are informative. Rice makes up 20% of the agricultural GDP in Nepal and 50% of the caloric intake. The study finds that that in the terai and hill regions there will be short term increases in the growing season. The terai will likely experience long term decreases in yields, there are mixed results in the hills. However in the mountains the short and long term yields will likely be increased. Although the results of the study are not completely certain the outcome tells us that there will be significant changes in where crops are grown in the future. This will likely result in land use change and possible migration and livelihood change.
Perhaps the greatest lesson that we can glean from climate models is that the climate is changing in complex ways and there is a large amount of uncertainty about the future of our climate. Action must be taken to address this threat. The focus must be on building resilience to stresses that the climate system may place on civilization. Although a government may not always be able to address a specific threat systems may be built up to respond to a diverse set of threats.

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