Between the 11th-16th March myself and four other PhDs and post docs attended the Ocean in Weather and Climate (OiWC) course at the Met Office, Exeter. This NERC advanced training course was aimed at PhDs, postdocs and beyond. It provided a great opportunity to spend a week meeting other Oceanography researchers at varying stages of their career, and to expand your understanding of the oceans role in climate beyond the scope of your own work.
The week kicked off with an ice breaker where we had do some ‘Scientific speed dating’, chatting to other participants about: Where are you from? What do you work on? What is your main hobby? What is the biggest question in your field of research? This set the tone for a very interactive week full of interesting discussions between all attendees and speakers alike. Course participants were accommodated at The Globe Inn situated in Topsham, a cute village-sized town full of pastel-coloured houses, cosy pubs, art galleries, and beautiful riverside walks to stretch your legs in the evenings.
The days consisted of four 1.5 hour sessions, split up by caffeine and biscuit breaks to recharge before the next session.
Topics covered in the lecture-style talks included…
Modelling the Ocean
High Resolution Ocean modelling in coupled forecast systems
The Meridional Overturning Circulation
The Southern Ocean in climate and climatic change
Climate variability on diurnal, seasonal, annual, inter-annual, decadal timescales
Climate sensitivity, heat uptake and sea level.
All the talks were very interesting and were followed by some stimulating discussion. Each session provided an overview of each topic and an indication of the current research questions in each area at the moment.
In the post lunch session, there were group practical sessions. These explored observational ARGO float data and model output. The practicals, written in iPython notebooks, were designed to let us play with some data, giving us a series of questions to trigger group discussions to deepen understanding of topics covered that morning.
The course also included some ‘softer’ evening talks, giving research career advice in a more informal manner. Most evenings were spent exploring the lovely riverside walks and restaurants/pubs of Topsham. The final evening was spent all together at the Cosy Club in Exeter, rounding off a very interesting and enjoyable week!
Modes of variability are climatological features that have global effects on regional climate and weather. They are identified through spatial structures and the timeseries associated with them (so-called EOF/PC analysis, which finds the largest variability of a given atmospheric field). Examples of modes of variability include El Niño Southern Oscillation, Madden-Julian Oscillation, North Atlantic Oscillation, Annular modes, etc. The latter are named after the “annulus” (a region bounded by two concentric circles) as they occur in the Earth’s midlatitudes (a band of atmosphere bounded by the polar and tropical regions, Fig. 1), and are the most important modes of midlatitude variability, generally representing 20-30% of the variability in a field.
We know two types of annular modes: baroclinic (based on eddy kinetic energy, a proxy for eddy activity and an indicator of storm-track intensity) and barotropic (based on zonal mean zonal wind, representing the north-south shifts of the jet stream) (Fig. 2). The latter are usually referred to as Southern (SAM or Antarctic Oscillation) or Northern (NAM or Arctic Oscillation) Annular Mode (depending on the hemisphere), have generally quasi-barotropic (uniform) vertical structure, and impact the temperature variations, sea-ice distribution, and storm paths in both hemispheres with timescales of about 10 days. The former are referred to as BAM (baroclinic annular mode) and exhibit strong vertical structure associated with strong vertical wind shear (baroclinicity), and their impacts are yet to be determined (e.g. Thompson and Barnes 2014, Marshall et al. 2017). These two modes of variability are linked to the key processes of the midlatitude tropospheric dynamics that are involved in the growth (baroclinic processes) and decay (barotropic processes) of midlatitude storms. The growth stage of the midlatitude storms is conventionally associated with increase in eddy kinetic energy (EKE) and the decay stage with decrease in EKE.
However, recent observational studies (e.g. Thompson and Woodworth 2014) have suggested decoupling of baroclinic and barotropic components of atmospheric variability in the Southern Hemisphere (i.e. no correlation between the BAM and SAM) and a simpler formulation of the EKE budget that only depends on eddy heat fluxes and BAM (Thompson et al. 2017). Using cross-spectrum analysis, we empirically test the validity of the suggested relationship between EKE and heat flux at different timescales (Boljka et al. 2018). Two different relationships are identified in Fig. 3: 1) a regime where EKE and eddy heat flux relationship holds well (periods longer than 10 days; intermediate timescale); and 2) a regime where this relationship breaks down (periods shorter than 10 days; synoptic timescale). For the relationship to hold (by construction), the imaginary part of the cross-spectrum must follow the angular frequency line and the real part must be constant. This is only true at the intermediate timescales. Hence, the suggested decoupling of baroclinic and barotropic components found in Thompson and Woodworth (2014) only works at intermediate timescales. This is consistent with our theoretical model (Boljka and Shepherd 2018), which predicts decoupling under synoptic temporal and spatial averaging. At synoptic timescales, processes such as barotropic momentum fluxes (closely related to the latitudinal shifts in the jet stream) contribute to the variability in EKE. This is consistent with the dynamics of storms that occur on timescales shorter than 10 days (e.g. Simmons and Hoskins 1978). This is further discussed in Boljka et al. (2018).
Boljka, L., and T. G. Shepherd, 2018: A multiscale asymptotic theory of extratropical wave, mean-flow interaction. J. Atmos. Sci., in press.
Boljka, L., T. G. Shepherd, and M. Blackburn, 2018: On the coupling between barotropic and baroclinic modes of extratropical atmospheric variability. J. Atmos. Sci., in review.
Marshall, G. J., D. W. J. Thompson, and M. R. van den Broeke, 2017: The signature of Southern Hemisphere atmospheric circulation patterns in Antarctic precipitation. Geophys. Res. Lett., 44, 11,580–11,589.
Simmons, A. J., and B. J. Hoskins, 1978: The life cycles of some nonlinear baroclinic waves. J. Atmos. Sci., 35, 414–432.
Thompson, D. W. J., and E. A. Barnes, 2014: Periodic variability in the large-scale Southern Hemisphere atmospheric circulation. Science, 343, 641–645.
Thompson, D. W. J., B. R. Crow, and E. A. Barnes, 2017: Intraseasonal periodicity in the Southern Hemisphere circulation on regional spatial scales. J. Atmos. Sci., 74, 865–877.
Thompson, D. W. J., and J. D. Woodworth, 2014: Barotropic and baroclinic annular variability in the Southern Hemisphere. J. Atmos. Sci., 71, 1480–1493.
When sea water freezes it forms sea ice, a composite of ice and brine. Sea ice exhibits varying structural, thermodynamic and mechanical properties across a range of length- and time-scales. It can be subcategorised into numerous different types of sea ice depending on where is grows and how old it is.
However, climate models do not simulate the evolution of floes (they model floes as cylindrical) or the floe size distribution, which has implications for ice melt rates and exchange of heat with the atmosphere and ocean. Sea ice also hosts algae and small organisms within brine channels in the ice, which can be important for nutrient cycles. This is a developing area of earth system modelling.
How much complexity do global climate models need to sufficiently model the interactions of sea ice with the ocean and atmosphere?
The representation of sea ice in global climate models is actually very simple, with minimal sea ice types and thickness categories. The main important feature of sea ice for global climate models is its albedo, which is much greater than that of open water, making it important for the surface energy balance. So, it is important to get the correct area of sea ice. Global climate models need sea ice:
to get the correct heat exchange with the atmosphere and ocean
to get a realistic overturning circulation in the ocean.
because salt release during sea ice growth is important for the ocean salinity structure, and therefore important to get the correct amount of sea in/near deep water formation sites.
sea ice is not important for sea level projections.
So, do the complex features of sea ice matter, or are simple parameterisations sufficient?
Schematic showing some dynamic features of sea ice 3.
Which leads to a lot more questions…
Where does the balance between sufficient complexity and computational cost lie?
Does adding extra model complexity actually make it harder to understand what the model is doing and therefore to interpret the results?
Do climate models need any further improvements to sea ice in order to better simulate global climate? There is still large uncertainty surrounding other climate model components, such as clouds and ocean eddies, which are believed to explain a lot of the discrepancy between models and observations, particularly in the Southern Ocean.
A lot of these questions depend on the scientific question that is being asked. And the question is not necessarily always ‘how is global climate going to change in the future’. Sea ice is fascinating because of its complexity, and there are still many interesting questions to investigate, hopefully before it all melts!
Images clockwise from top left: grease ice 4, pancake ice 5, surface melt ponds 6, ice floes 7.
The Future Developments in Climate Sea Ice Modelling Workshop
This blog stems from a one day workshop I attended on ‘Future developments in climate sea ice modelling’ at the Isaac Newton Centre as part of a four month programme on the ‘Mathematics of Sea Ice Phenomena’. The format of the day was that three different strands of sea ice researchers gave 40 min talks giving their strand’s point of view of current sea ice developments and what the focus should be for sea ice modelers, each followed by 40 mins of open discussion with the audience.
The three (very good!) talks were:
Dirk Notz: What do climate models need sea ice for? A top-down, system level view of what sea ice models should produce from the perspective of a climate modeller.
Cecilia Bitz: What sea ice physics is missing from models? A bottom-up view of what is missing from current sea ice models from the perspective of a sea ice scientist.
Elizabeth Hunke: What modelling approaches can be used to address the complexity of sea ice and the needs of climate models?
For many Africans, the timing of the wet season is of crucial importance, especially for those reliant upon subsistence agriculture, who depend on the seasonal rains for crop irrigation. In addition, the wet season recharges lakes, rivers and water storage tanks which constitute the domestic water supply in some areas. The timing of the wet season also affects the availability of energy from hydroelectric schemes, and has impacts upon the prevalence of certain disease carrying vectors, such as mosquitoes.
Climate change is already threatening many vulnerable populations, and changes in the timing or intensity of the wet season, or increasing uncertainty in the timing of the onset, may lead to significant socio-economic impacts. But before we consider future projections or past changes in the seasonality, we need to go back a few steps.
The first step is to find a method for determining when the wet season starts and ends (its ‘onset’ and ‘cessation’). In order to look at large-scale shifts in the timing of the wet season and relate this to wider-scale drivers, this method needs to be applicable across the entirety of continental Africa. Most previous methods for determining the onset focus on the national to regional scale, and are dependent on the exceedance of a certain threshold e.g. the ﬁrst week with at least 20mm of rainfall, with one rainfall event of more than 10mm, and no dry spell of more than 10 days after the rain event for the next month. While such definitions work well at a national scale they are not applicable at a continental scale where rainfall amounts vary substantially. A threshold suitable for the dry countries at the fringes of the Sahara would not be suitable in the wetter East African highlands.
In addition to a vast range of rainfall amounts, the African continent also spans multiple climatic regimes. The seasonal cycle of precipitation over continental Africa is largely driven by the seasonal progression of the ITCZ and associated rain belts, which follows the maximum incoming solar radiation. In the boreal summer, when the thermal equator sits between the equator and the Tropic of Cancer, the ITCZ sits north of the equator and West Africa and the Sahel experience a wet season. During the boreal autumn the ITCZ moves south, and southern Africa experiences a wet season during the austral summer, followed by the northward return of the ITCZ during the boreal spring. As a consequence of this, central African regions and the Horn of Africa experience two wet seasons per year – one as the ITCZ travels north, and a second as the ITCZ travels south. A method for determining the onset and cessation at the continental scale thus needs to account for regions with multiple wet seasons per year.
In our paper (available here) we propose such a method, based on the method of Liebmann et al (2012). The method has three steps:
Firstly, determine the number of seasons experienced per year at the location (or grid point) of interest. This is achieved using harmonic analysis – the amplitude of the first and second harmonic were computed, using the entire timeseries and their ratio compared. If the ratio was greater than 1.0, i.e. the amplitude of the second harmonic was greater than the amplitude of the first harmonic then the grid point was defined as having two wet seasons per year (biannual), if the ratio was less than one then it was defined as having an annual regime. Figure 1 shows the ratio for one African rainfall dataset (TARCATv2). Three regions are identified as biannual regions; the Horn of Africa, an equatorial strip extending from Gabon to Uganda and a small region on the southern West African coastline.
Secondly the period of the year when the wet season occurs was determined. This was achieved by looking for minima and maxima in the climatological cumulative daily rainfall anomaly to identify one or two seasons.
The third and final stage is to calculate the onset and cessation dates for each year. This is done by looking for the minima and maxima in the cumulative daily rainfall anomaly, calculated for each season.
Figure 2 shows the seasonal progression of the onset and cessation, with the patterns observed in agreement with those expected from the driving physical mechanisms, and continuous progression across the annual/biannual boundaries. Over West Africa and the Sahel, Figure 2a-b shows zonally-contiguous progression patterns with onset following the onset of the long rains and moving north, and cessation moving southward, preceding the end of the short rains. Over southern Africa Figure 2c-d shows the onset over southern Africa starting in the north-west and south-east, following the onset of the short rains, reaching the East African coast last, and cessation starting at the Zimbabwe, Mozambique, South Africa border and spreading out radially into the cessation of the long rains.
As well as testing the method for compatibility with known physical drivers of African rainfall, agreement across multiple satellite-based rainfall estimates was also examined. In general, good agreement was found across the datasets, particularly for regions with an annual regime and over the biannual region of East Africa.
The advantage of having a method that works at the continental scale is the ability to look at the impact of large-scale oscillations on wider-scale variability. One application of this method was to investigate the impact of El Niño upon both the annual rains and short rains (Figure 3). In Figure 3 we see the well-documented dipole in rainfall anomaly, with higher rainfall totals over 0–15°S and the Horn of Africa in El Niño years and the opposite between 15°S and 30°S. This anomaly is stronger when we use this method compared with using standard meteorological seasons. We can also see that while the lower rainfall to the south is colocated with later onset dates and a consequentially shorter season, the higher rainfall over the Horn of Africa is associated with later cessation of the short rains, with only small differences in onset date.
In addition to using this method for research purposes, its application within an operational setting is also being explored. Hopefully, the method will be included within the Rainwatch platform, which will be able to provide users with a probabilistic estimate of whether or not the season has started, based on the rainfall experienced so far that year, and historical rainfall data.
For more details, please see the paper detailing this work:
Dunning, C.M., E Black, and R.P. Allan (2016) The onset and cessation of seasonal rainfall over Africa, Journal of Geophysical Research: Atmospheres, 121 11,405-11,424, doi: 10.1002/2016JD025428
Liebmann, B., I. Bladé, G. N. Kiladis, L. M. Carvalho, G. B. Senay, D. Allured, S. Leroux, and C. Funk (2012), Seasonality of African precipitation from 1996 to 2009, J. Clim., 25(12), 4304–4322.
Gristey, J. J., J. C. Chiu, R. J. Gurney, S.-C. Han, and C. J. Morcrette (2017), Determination of global Earth outgoing radiation at high temporal resolution using a theoretical constellation of satellites, J. Geophys. Res. Atmos., 122, doi:10.1002/2016JD025514.
The surface of our planet has warmed at an unprecedented rate since the mid-19th century and there is no sign that the rate of warming is slowing down. The last three decades have all been successively warmer than any preceding decade since 1850, and 16 of the 17 warmest years on record have all occurred since 2001. The latest science now tells us that it is extremely likely that human influence has been the dominant cause of the observed warming1, mainly due to the release of carbon dioxide and other greenhouse gases into our atmosphere. These greenhouse gases trap heat energy that would otherwise escape to space, which disrupts the balance of energy flows at the top of the atmosphere (Fig. 1). The current value of the resulting energy imbalance is approximately 0.6 W m–2, which is more than 17 times larger than all of the energy consumed by humans2! In fact, observing the changes in these energy flows at the top of the atmosphere can help us to gauge how much the Earth is likely to warm in the future and, perhaps more importantly, observations with sufficient spatial coverage, frequency and accuracy can help us to understand the processes that are causing this warming.
Observations of energy flows at the top of the atmosphere have traditionally been made by large and expensive satellites that may be similar in size to a large car3, making it impractical to launch multiple satellites at once. Although such observations have led to many advancements in climate science, the fundamental sampling restrictions from a limited number of satellites makes it impossible to fully resolve the variability in the energy flows at the top of atmosphere. Only recently, due to advancements in small satellite technology and sensor miniaturisation, has a novel, viable and sustainable sampling strategy from a constellation of satellites become possible. Importantly, a constellation of small satellites (Fig. 2a), each the size of a shoe-box (Fig. 2b), could provide both the spatial coverage and frequency of sampling to properly resolve the top of atmosphere energy flows for the first time. Despite the promise of the constellation approach, its scientific potential for measuring energy flows at the top of the atmosphere has not been fully explored.
To explore this potential, several experiments have been performed that simulate measurements from the theoretical constellation of satellites shown in Fig 2a. The results show that just 1 hour of measurements can be used to reconstruct accurate global maps of reflected sunlight and emitted heat energy (Fig. 3). These maps are reconstructed using a series of mathematical functions known as “spherical harmonics”, which extract the information from overlapping samples to enhance the spatial resolution by around a factor of 6 when compared with individual measurement footprints. After producing these maps every hour during one day, the uncertainty in the global-average hourly energy flows is 0.16 ± 0.45 W m–2 for reflected sunlight and 0.13 ± 0.15 W m–2 for emitted heat energy. Observations with these uncertainties would be capable of determining the sign of the 0.6 W m–2 energy imbalance directly from space4, even at very short timescales.
Also investigated are potential issues that could restrict similar uncertainties being achieved in reality such as instrument calibration and a reduced number of satellites due to limited resources. Not surprisingly, the success of the approach will rely on calibration that ensures low systematic instrument biases, and on a sufficient number of satellites that ensures dense hourly sampling of the globe. Development and demonstration of miniaturised satellites and sensors is currently underway to ensure these criteria are met. Provided good calibration and sufficient satellites, this study demonstrates that the constellation concept would enable an unprecedented sampling capability and has a clear potential for improving observations of Earth’s energy flows.
This work was supported by the NERC SCENARIO DTP grant NE/L002566/1 and co-sponsored by the Met Office.
2 Total energy consumed by humans in 2014 was 13805 Mtoe = 160552.15 TWh. This is an average power consumption of 160552.15 TWh / 8760 hours in a year = 18.33 TW
Rate of energy imbalance per square metre at top of atmosphere is = 0.6 W m–2. Surface area of “top of atmosphere” at 80 km is 4 * pi * ((6371+80)*103 m)2 = 5.23*1014 m2. Rate of energy imbalance for entire Earth = 0.6 W m–2 * 5.23*1014 m2 = 3.14*1014 W = 314 TW
Multiples of energy consumed by humans = 314 TW / 18.33 TW = 17
3 The satellites currently carrying instruments that observe the top of atmosphere energy flows (eg. MeteoSat 8, Aqua) will typically also be hosting a suite of other instruments, which adds to the size of the satellite. However, even the individual instruments are still much larger that the satellite shown in Fig. 2b.
4 Currently, the single most accurate way to determine the top-of-atmosphere energy imbalance is to infer it from changes in ocean heat uptake. The reasoning is that the oceans contain over 90% of the heat capacity of the climate system, so it is assumed on multi-year time scales that excess energy accumulating at the top of the atmosphere goes into heating the oceans. The stated value of 0.6 W m–2 is calculated from a combination of ocean heat uptake and satellite observations.
Allan et al. (2014), Changes in global net radiative imbalance 1985–2012, Geophys. Res. Lett., 41, 5588–5597, doi:10.1002/2014GL060962.
Barnhart et al. (2009), Satellite miniaturization techniques for space sensor networks, Journal of Spacecraft and Rockets, 46(2), 469–472, doi:10.2514/1.41639.
Swartz et al. (2016), The Radiometer Assessment using Vertically Aligned Nanotubes (RAVAN) CubeSat Mission: A Pathfinder for a New Measurement of Earth’s Radiation Budget. Proceedings of the AIAA/USU Conference on Small Satellites, SSC16-XII-03
Sulphate aerosol injection (SAI) is one of the geoengineering proposals that aim to reduce future surface temperature rise in case ambitious carbon dioxide mitigation targets cannot be met. Climate model simulations suggest that by injecting 5 teragrams (Tg) of sulphur dioxide gas (SO2) into the stratosphere every year, global surface cooling would be observed within a few years of implementation. This injection rate is equivalent to 5 million tonnes of SO2 per year, or one Mount Pinatubo eruption every 4 years (large volcanic eruptions naturally inject SO2 into the stratosphere; the Mount Pinatubo eruption in 1991 led to ~0.5 °C global surface cooling in the 2 years that followed (Self et al., 1993)). However, temperature fluctuations occur naturally in the climate system too. How could we detect the cooling signal of SAI amidst internal climate variability and temperature changes driven by other external forcings?
The answer to this is optimal fingerprinting (Allen and Stott, 2003), a technique which has been extensively used to detect and attribute climate warming to human activities. Assuming a scenario (G4, Kravitz et al., 2011) in which 5 Tg yr-1 of SO2 is injected into the stratosphere on top of a mid-range warming scenario called RCP4.5 from 2020-2070, we first estimate the climate system’s internal variability and the temperature ‘fingerprints’ of the geoengineering aerosols and greenhouse gases separately, and then compare observations to these fingerprints using total least squares regression. Since there are no real-world observations of geoengineering, we cross-compare simulations from different climate models in this research. This gives us 44 comparisons in total, and the number of years that would be needed to robustly detect the cooling signal of SAI in global-mean near-surface air temperature is estimated for each of them.
Figure 1(a) shows the distribution of the estimated time horizon over which the SAI cooling signal would be detected at the 10% significance level in these 44 comparisons. In 29 of them, the cooling signal would be detected during the first 10 years of SAI implementation. This means we would not only be able to separate the cooling effect of SAI from the climate system’s internal variability and temperature changes driven by greenhouse gases, but we would also be able to achieve this early into SAI deployment.
The above results are tested by applying a variant of optimal fingerprinting to the same problem. This new method assumes a non-stationary background climate that is mainly forced by greenhouse gases, and attempts to detect the cooling effect of SAI against the warming background using regression (Bürger and Cubasch, 2015). Figure 1(b) shows the distribution of the detection horizons estimated by using the new method in the same 44 comparisons: 35 comparisons would require 10 years or fewer for the cooling signal to be robustly detected. This shows a slight improvement from the results found with the conventional method, but the two distributions are very similar.
To conclude, we would be able to separate and thus, detect the cooling signal of sulphate aerosol geoengineering from internal climate variability and greenhouse gas driven warming in global-mean temperature within 10 years of SAI deployment in a future 5 Tg yr-1 SAI scenario. This could be achieved with either the conventional optimal fingerprinting method or a new, non-stationary detection method, provided that the climate data are adequately filtered. Research on the effects of different data filtering techniques on geoengineering detectability is not included in this blog post, please refer to the article cited at the top for more details.
This work has been funded by the University of Reading. Support has also been provided by the UK Met Office.
Note: So how feasible is a 5 Tg yr-1 SO2 injection scenario? Robock et al. (2009) estimated the cost of lofting 1 Tg yr-1 SO2 into the stratosphere with existing aircrafts to be several billion U.S. dollars per year. Scaling this to 5 Tg yr-1 is still not a lot compared to the gross world product. There are practical issues to be addressed even if existing aircrafts were to be used for SAI, but the deciding factor of whether to implement sulphate aerosol geoengineering or not would likely be its potential benefits and side effects, both on the climate system and the society.
Self, Stephen, et al. “The atmospheric impact of the 1991 Mount Pinatubo eruption.” (1993).
Allen, M. R., and P. A. Stott. “Estimating signal amplitudes in optimal fingerprinting, Part I: Theory.” Climate Dynamics 21.5-6 (2003): 477-491.
Kravitz, Ben, et al. “The geoengineering model intercomparison project (GeoMIP).” Atmospheric Science Letters 12.2 (2011): 162-167.
Bürger, Gerd, and Ulrich Cubasch. “The detectability of climate engineering.” Journal of Geophysical Research: Atmospheres 120.22 (2015).
Robock, Alan, et al. “Benefits, risks, and costs of stratospheric geoengineering.” Geophysical Research Letters 36.19 (2009).
Over the past two weeks 25,000 delegates have been gathering in Marrakech to discuss mitigation and adaptation for climate change. On the 4th November 2016 the Paris Agreement came into force and as a result discussions during the conference debated its implementation. The Walker Institute and the Department of Meteorology (University of Reading), with the support of the NERC SCENARIO doctoral training partnership and an UNFCCC partnership, supported two PhD students to be official UN observers at COP22, and enabled remote participation with students back at Reading University. To find out more about our work with COP22 continue reading this blog post and check out:
Today (18/11/16) the UK government are set to announce that the United Kingdom has ratified the Paris Agreement. Yesterday, Boris Johnson (UK foreign secretary) signed the Paris Agreement after no objections were raised by the House of Commons or House of Lords. The United Kingdom in accordance with the Intended Nationally Determined Contributions (INDCs) of the European Union, are set to reduce greenhouse gas emissions by 40% by 2030 relative to 1990 emission levels. Today also marks the end of the 22nd Conference of the Parties (COP) for the United Nations Framework Convention on Climate Change and here are some quick summary points that PhD students took away from observing the process in Marrakech:
1) The significance of the Paris Agreement.
“Now that we have Paris, we need to take action immediately”
Teresa Anderson, ActionAid UK.
The Paris Agreement marks a change in the intentions during the COP process. Due to the success and ratification of the Paris Agreement more discussions can be based on the adaptation and mitigation against climate change, rather than negotiating global targets on climate change prevention. The Paris Agreement states that a global response is needed to respond to the threat of climate change and that global temperature rise should be kept well below 2°C and that efforts should be pursued to limit the global temperature rise to 1.5°C. COP22 Marrakech, began by stating that this is the “COP of Action”, and therefore the focus seen during side events, negotiations, dignitary speeches and press conferences was on the need for action.
“Countries have strongly supported the [Paris] Agreement because they realize their own national interest is best secured by pursuing the common good. Now we have to translate words into effective policies and actions.”
Mr Ban Ki-Moon, Secretary General of the United Nations.
2) A continued effort is needed to concentrate on the individual.
As SCENARIO PhD students we were challenged to understand the process that takes place during a UNFCCC conference. To do this we interviewed many conference delegates including policymakers, research organisations, industry experts, entrepreneurs, environmental consultants and funding sources to name a few. A common theme that ran through most of our interviews is that action is needed to prioritise the individual as well as thinking in terms of national- and community-level. To ensure the successful mitigation and adaptation to climate change, strategies need to come into place that protect the rights of the individual. This poses a global challenge, stretching from protecting the livelihoods of indigenous cultures and those impacted by sea level rise on low-lying islands, to supporting workers who rely on the non-renewable energy industry. In terms of climate research we need to ensure that we make our scientific conclusions accessible on an individual-level so that our work has a greater impact.
“a key goal for us is making climate change research accessible to the user community”
3) Action is needed now, however the Paris Agreement only implies action post-2020.
Throughout our attendance in plenary meetings and side events there was an emphasis that whilst the Paris Agreement is an important stepping stone to combatting climate change, action is needed before 2020 for the Paris Agreement to be reached. Currently INDCs are proposed for between 2021-2030, however for the intended global temperature targets to be achieved it was argued that action is needed now. Although, pre-2020 action raises much contention, with the most popular argument against pre-2020 action being that more time and effort is needed for negotiations to ensure that a better understanding of national efforts to climate change mitigation is determined.
“We need to take action before 2020. Working for action post-2020 is not going to be enough. We need to start acting now.”
Honduras Party Representative.
“We need more time to work on the rule book for the Paris Agreement. Discussions on this should continue.”
Switzerland Party Representative.
4) There is a difference in opinion on whether 1.5°C can be reached.
For me the most interesting question we asked conference delegates was “do you think the target of 1.5°C can be reached?” This question brought a difference of opinion including some party members arguing that the change in our non-renewable energy dependence is far too great for the target to be achieved. Meanwhile, other political representatives and NGO delegates argued that accepting the target is unachievable before even trying makes negotiations and discussions less successful. There was also anticipation for the future IPCC report titled, Special Report on Global Warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways.
“Of course we want to fight for 1.5°C, why fight for 2°C? It just makes sense to fight for 1.5°C”
Martina Duncan, Party Representative for Grenada.
COP22 has been a fantastic opportunity for PhD students in our department to interact and understand the process that takes place during a UNFCCC conference. Whilst the past couple of weeks have been dominated by the results of the US election and the associated uncertainties, there has been an increasing global recognition of climate change and that action should be taken. In the next few years the challenge to mitigate and adapt towards climate change will be an increasing priority, and let us hope that these annual UNFCCC conferences are key stepping stones for climate change action.
“This is a problem people are recognising, and that it is time to change”
Jonathan Pershing, US Climate Envoy
Thank you all those who have supported our work at COP22 this year. Thank you to the Walker Institute, NERC SCENARIO doctoral training partnership and UNFCCC for this brilliant opportunity. Thank you to all those who have supported us with publicity including NERC, Royal Meteorological Society, members of staff and PhD students at the University of Reading and Lucy Wallace who has ensured the appropriate communication of our project. Plus a huge thanks to all delegates and staff at COP22 who volunteered their time to talk to us.