Generating Regional Emissions Estimates with a Novel Hierarchy of Observations and Upscaled Simulation Experiments (GREENHOUSE)
Lead Research Organisation:
University of Edinburgh
Department Name: Sch of Geosciences
Abstract
The UK is committed to quantifying and managing its emissions of greenhouse gases (GHG, i.e. CO2, CH4, N2O) to reduce the threat of dangerous climate change. Sinks and sources of GHGs vary in space and time across the UK because of the landscape's mosaic of managed and semi-natural ecosystems, and the varying temporal sensitivities of each GHG's emissions to meteorology and management. Understanding spatio-temporal patterns of biogenic GHG emissions will lead to improvements in flux estimates, allow creation of inventories with greater sensitivity to management and climate, and advance the modelling of feedbacks between climate, land use and GHG emissions. Addressing Deliverable C of the NERC Greenhouse Gas Emissions and Feedbacks Research Programme, we will use extensive existing UK field data on GHG emissions, supplemented with targeted new measurements at a range of scales, to build accurate GHG inventories and improve the capabilities of two land surface models (LSMs) to estimate GHG emissions.
Our measurements will underpin state-of-the-art temporal and spatial upscaling frameworks. The temporal framework will evaluate diurnal, seasonal and inter-annual variation in emissions of CO2, CH4 and N2O over dominant UK land-covers, resolving management interventions such as ploughing, fertilizing and harvesting, and the effects of weather and climate variability. The spatial framework will evaluate landscape heterogeneity at patch (m), field (ha) and landscape (km2) scales, in two campaigns combining chambers, tower and airborne flux measurements in arable croplands of eastern England, and grazing and forest landscapes of northern Britain.
For modelling, we will update two LSMs - JULES and CTESSEL- so that each generates estimates of CO2, CH4 and N2O fluxes from managed landscapes. The models will be updated to include the capabilities to represent changes in land use over time, to represent changes in land management over time (crop sowing, fertilizing, harvesting, ploughing etc), and the capacity to simulate forest rotations. With these changes in place, we will determine parameterisations for dominant UK land-covers and management interventions, using our spatio-temporal data.
The work is organized in five science work-packages (WP).
WP1: Data assembly and preliminary analysis. We will create a database of GHG flux data and ancillary data for major UK landcovers/landuses in order to calibrate and evaluate the LSMs' capabilities, and generate spatial databases of environmental and management drivers for the models.
WP2. GHG measurement at multiple scales. We will deploy advanced technology to generate new information on spatial GHG processes from simultaneous measurement from chamber (<1 m) to landscape (40 km) length scales, and on temporal flux variation from minutes to years.
WP3. Earth observation (EO) to support upscaling. EO data will provide: i) driving data for LSM upscaling, from flux tower to aircraft campaign scales; and ii) spatial data for testing LSM outputs at these larger scales.
WP4 Upscaling GHG processes. Firstly, the two LSMs will be updated to allow the impacts of management activities on GHG emissions to be simulated, with calibration against an array of temporal flux data. Then, we will use the LSMs to model the fluxes of GHGs at larger spatial scales, based on a rigorous understanding of how the nonlinearity of responses and the non-Gaussian distribution of environmental input variables interact, for each GHG, using all available field data at finer scales.
WP5 Application at the regional scale. The LSMs will upscale GHG emissions for both campaign regions (E. England, N. Britain) using 1-km2 resolution simulations with a focus on the airborne campaign periods of 4 weeks. We will determine how regional upscaling error can be reduced with intensive spatial soil and land management data.
Our measurements will underpin state-of-the-art temporal and spatial upscaling frameworks. The temporal framework will evaluate diurnal, seasonal and inter-annual variation in emissions of CO2, CH4 and N2O over dominant UK land-covers, resolving management interventions such as ploughing, fertilizing and harvesting, and the effects of weather and climate variability. The spatial framework will evaluate landscape heterogeneity at patch (m), field (ha) and landscape (km2) scales, in two campaigns combining chambers, tower and airborne flux measurements in arable croplands of eastern England, and grazing and forest landscapes of northern Britain.
For modelling, we will update two LSMs - JULES and CTESSEL- so that each generates estimates of CO2, CH4 and N2O fluxes from managed landscapes. The models will be updated to include the capabilities to represent changes in land use over time, to represent changes in land management over time (crop sowing, fertilizing, harvesting, ploughing etc), and the capacity to simulate forest rotations. With these changes in place, we will determine parameterisations for dominant UK land-covers and management interventions, using our spatio-temporal data.
The work is organized in five science work-packages (WP).
WP1: Data assembly and preliminary analysis. We will create a database of GHG flux data and ancillary data for major UK landcovers/landuses in order to calibrate and evaluate the LSMs' capabilities, and generate spatial databases of environmental and management drivers for the models.
WP2. GHG measurement at multiple scales. We will deploy advanced technology to generate new information on spatial GHG processes from simultaneous measurement from chamber (<1 m) to landscape (40 km) length scales, and on temporal flux variation from minutes to years.
WP3. Earth observation (EO) to support upscaling. EO data will provide: i) driving data for LSM upscaling, from flux tower to aircraft campaign scales; and ii) spatial data for testing LSM outputs at these larger scales.
WP4 Upscaling GHG processes. Firstly, the two LSMs will be updated to allow the impacts of management activities on GHG emissions to be simulated, with calibration against an array of temporal flux data. Then, we will use the LSMs to model the fluxes of GHGs at larger spatial scales, based on a rigorous understanding of how the nonlinearity of responses and the non-Gaussian distribution of environmental input variables interact, for each GHG, using all available field data at finer scales.
WP5 Application at the regional scale. The LSMs will upscale GHG emissions for both campaign regions (E. England, N. Britain) using 1-km2 resolution simulations with a focus on the airborne campaign periods of 4 weeks. We will determine how regional upscaling error can be reduced with intensive spatial soil and land management data.
Planned Impact
To exchange knowledge with policy makers and land managers, we will host a workshop to explain the novel science outputs relating GHG emissions to managed landscapes, with attendees from a range of government departments, governmental organisations, NGOs and agricultural and forest industry bodies. We will also provide evidence at the relevant Westminster and Holyrood Parliamentary committees and for the Scottish ClimateXChange. We will disseminate our research findings to land managers at UK and European land management conferences. An improved understanding of the linkage between landscape management and GHG emissions will be used to develop improved advice on mitigation to UK farmers and land managers.
To put our science in a global context, we will host a science workshop on measuring and modelling GHG emissions in managed landscapes, inviting our international partners. To generate links to climate modelling groups, we will work closely with UKMO and ECMWF to upgrade JULES and CTESSEL for their applications.
Our work will be of interest to those members of the public concerned about climate security and land management. We will engage with local communities around our research sites, to explain our activities and to learn more about local perspectives. We will create press releases, a website and use social media to communicate with the wider public.
We will contribute to the development of a more scientifically literate population through relating our research and core concepts in environmental science to school teachers and pupils. Teaching material will be developed based on our data, emphasising our cutting edge technology (e.g. our aircraft), at the relevant level and to fit with the relevant strands and learning outcomes of the Curriculum for Excellence. Material developed here can also be shown at the Edinburgh International Science Fair.
To summarise, we will use the press, a project website and social media for broad dissemination of data. We will make presentations to local UK communities during field campaigns. We will host two workshops with involvement of academic and non-academic project partners. We will create new materials and activities with schools, and present at a Science Fair. We will provide evidence to parliamentary committees, report to DEFRA, DECC and NGOs.
To put our science in a global context, we will host a science workshop on measuring and modelling GHG emissions in managed landscapes, inviting our international partners. To generate links to climate modelling groups, we will work closely with UKMO and ECMWF to upgrade JULES and CTESSEL for their applications.
Our work will be of interest to those members of the public concerned about climate security and land management. We will engage with local communities around our research sites, to explain our activities and to learn more about local perspectives. We will create press releases, a website and use social media to communicate with the wider public.
We will contribute to the development of a more scientifically literate population through relating our research and core concepts in environmental science to school teachers and pupils. Teaching material will be developed based on our data, emphasising our cutting edge technology (e.g. our aircraft), at the relevant level and to fit with the relevant strands and learning outcomes of the Curriculum for Excellence. Material developed here can also be shown at the Edinburgh International Science Fair.
To summarise, we will use the press, a project website and social media for broad dissemination of data. We will make presentations to local UK communities during field campaigns. We will host two workshops with involvement of academic and non-academic project partners. We will create new materials and activities with schools, and present at a Science Fair. We will provide evidence to parliamentary committees, report to DEFRA, DECC and NGOs.
Organisations
- University of Edinburgh (Lead Research Organisation)
- NatureScot (Scottish Natural Heritage) (Project Partner)
- Laboratoire des Sciences du Climat et de l'Environnement (Project Partner)
- Oregon State University (Project Partner)
- Agricultural Industries Confederation (Project Partner)
- Scottish Environment Protection Agency (Project Partner)
- Natural England (Project Partner)
- Teagasc - The Irish Agriculture and Food Development Authority (Project Partner)
- Freie Universität Berlin (Project Partner)
- Met Office (Project Partner)
- Technical University of Denmark (Project Partner)
- Forestry Commission England (Project Partner)
- Committee on Climate Change (Project Partner)
- Max Planck Institutes (Project Partner)
- RSPB Scotland (Project Partner)
- Institut Pierre-Simon Laplace (Project Partner)
- Lund University (Project Partner)
- International Union for Conservation of Nature (Project Partner)
- Department for Environment Food and Rural Affairs (Project Partner)
- Department for Business, Energy and Industrial Strategy (Project Partner)
- European Centre for Medium-Range Weather Forecasts (Project Partner)
- Tilhill Forestry (Project Partner)
- Karlsruhe Institute of Technology (Project Partner)
Publications
Smallman T
(2022)
From Ecosystem Observation to Environmental Decision-Making: Model-Data Fusion as an Operational Tool
in Frontiers in Forests and Global Change
Slevin D
(2017)
Global evaluation of gross primary productivity in the JULES land surface model v3.4.1
in Geoscientific Model Development
Famiglietti CA
(2023)
Global net biome CO2 exchange predicted comparably well using parameter-environment relationships and plant functional types.
in Global change biology
Safta C
(2015)
Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model
in Geoscientific Model Development
Exbrayat J
(2017)
Impact of deforestation and climate on the Amazon Basin's above-ground biomass during 1993-2012
in Scientific Reports
Revill A
(2016)
Impacts of reduced model complexity and driver resolution on cropland ecosystem photosynthesis estimates
in Field Crops Research
Myrgiotis V
(2018)
Improving model prediction of soil N2O emissions through Bayesian calibration.
in The Science of the total environment
Barnhill K
(2022)
Incorporating dead material in ecosystem assessments and projections
in Nature Climate Change
Myrgiotis V
(2021)
Inferring management and predicting sub-field scale C dynamics in UK grasslands using biogeochemical modelling and satellite-derived leaf area data
in Agricultural and Forest Meteorology
George-Chacon S
(2023)
Isolating the effects of land use and functional variation on Yucatán's forest biomass under global change
in Frontiers in Forests and Global Change
Bloom A
(2020)
Lagged effects regulate the inter-annual variability of the tropical carbon balance
in Biogeosciences
Butler EE
(2017)
Mapping local and global variability in plant trait distributions.
in Proceedings of the National Academy of Sciences of the United States of America
Myrgiotis V
(2016)
Model evaluation in relation to soil N2O emissions: An algorithmic method which accounts for variability in measurements and possible time lags
in Environmental Modelling & Software
Bonan G
(2014)
Modeling stomatal conductance in the earth system: linking leaf water-use efficiency and water transport along the soil-plant-atmosphere continuum
in Geoscientific Model Development
Slevin D
(2015)
Multi-site evaluation of the JULES land surface model using global and local data
in Geoscientific Model Development
Chew Y
(2015)
Multiscale digital Arabidopsis predicts individual organ and whole-organism growth (vol 111, pg E4127, 2014)
in Proceedings of the National Academy of Sciences of the United States of America
Chew YH
(2014)
Multiscale digital Arabidopsis predicts individual organ and whole-organism growth.
in Proceedings of the National Academy of Sciences of the United States of America
White E
(2019)
Quantifying the UK's carbon dioxide flux: an atmospheric inverse modelling approach using a regional measurement network
in Atmospheric Chemistry and Physics
Exbrayat J
(2018)
Reliability ensemble averaging of 21st century projections of terrestrial net primary productivity reduces global and regional uncertainties
in Earth System Dynamics
Milodowski D
(2023)
Scale variance in the carbon dynamics of fragmented, mixed-use landscapes estimated using model-data fusion
in Biogeosciences
Rosan T
(2024)
Synthesis of the land carbon fluxes of the Amazon region between 2010 and 2020
in Communications Earth & Environment
Description | The development and integration of number of measuring systems on the School of GeoSciences research aircraft has been at least part funded (especially in terms of staff time) by this project. These include a meteorological system, to measure the temperature, humidity and pressure, as well as turbulence; a gas analysis system, measuring methane, carbon dioxide and water at very high precision in real time; and a bag sampling system allowing us to bring samples back to the lab for analysis of all sorts of chemicals of interest to climate scientists. These systems provide us with unique capabilities to explore the exchange of greenhouse gases between the land surface and the atmosphere, and hence to test and develop our large scale models of the processes involved. We have developed capacity to calibrate our carbon cycle model for managed forests, using earth observations, forestry data, and ecological measurements. We have produced a 1 km resolution analysis of UK forest carbon cycling for 2000-15. This analysis integrates soil maps of C, land use data from the Forestry Commission, satellite estimates of leaf area index and biomass, and climate with a model of C dynamics. Maps are produced with clear estimates of uncertainty. A similar activity at 0.5 degree resolution for Great Britain has estimated C cycling using similar inputs, and successfully validated against independent data from tall towers. Working with the larger team, we are producing high resolution maps of N2O emissions from agriculture. These emission maps build on high resolution model outputs of soil temperature and moisture, linked to maps of N inputs (organic and inorganic), crop type, and soil type. The outcome is a robust analysis of emissions likelihoods. |
Exploitation Route | Our C modelling allows assessment of regional UK carbon balance in managed systems (e.g. UK) with quantified uncertainty, with value for land managers and for greenhouse gas accounting. Our N2O modelling allows detailed assessment of patterns of N2O emissions across the UK, with value for policy makers in determination of mitigation strategies. |
Sectors | Agriculture Food and Drink Environment Government Democracy and Justice |
Description | Our forest model system contributed to a TSB grant, working with SMEs to develop biomass production services for the energy sector. Our forest model system is contributing to an industry led activity supporting improved forest management in six tropical countries, linked to UK Space Agency. |
First Year Of Impact | 2019 |
Sector | Education,Energy,Environment |
Impact Types | Cultural |
Description | Agri-Tech in China Newton Network+ |
Amount | £124,413 (GBP) |
Funding ID | LG007 |
Organisation | Rothamsted Research |
Sector | Academic/University |
Country | United Kingdom |
Start | 04/2018 |
End | 03/2019 |
Description | CSSP Brazil BZ2: Evaluation of South American Ecosystem Processes |
Amount | £250,000 (GBP) |
Organisation | Newton Fund |
Sector | Public |
Country | United Kingdom |
Start | 03/2019 |
End | 03/2021 |
Description | DataLab |
Amount | £90,000 (GBP) |
Organisation | Government of Scotland |
Sector | Public |
Country | United Kingdom |
Start | 03/2017 |
End | 03/2018 |
Description | Detection and Attribution of Regional greenhouse gas Emissions in the UK (DARE-UK) |
Amount | £616,947 (GBP) |
Funding ID | NE/S003819/1 |
Organisation | Natural Environment Research Council |
Sector | Public |
Country | United Kingdom |
Start | 02/2019 |
End | 01/2024 |
Description | Global Challenges |
Amount | £110,000 (GBP) |
Organisation | The Royal Society |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 01/2017 |
End | 01/2018 |
Description | IPP |
Amount | £16,000,000 (GBP) |
Organisation | UK Space Agency |
Sector | Public |
Country | United Kingdom |
Start | 12/2016 |
End | 11/2019 |
Description | International Partnership Space Programme |
Amount | £141,250 (GBP) |
Organisation | UK Space Agency |
Sector | Public |
Country | United Kingdom |
Start | 05/2015 |
End | 03/2016 |
Description | Newton |
Amount | £300,000 (GBP) |
Organisation | Meteorological Office UK |
Sector | Academic/University |
Country | United Kingdom |
Start | 03/2017 |
End | 03/2019 |
Description | SARIC |
Amount | £950,000 (GBP) |
Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
Sector | Public |
Country | United Kingdom |
Start | 12/2016 |
End | 11/2020 |
Description | The Data Lab/GSi |
Amount | £99,012 (GBP) |
Funding ID | UoE/GSI/TDL |
Organisation | Government of Scotland |
Sector | Public |
Country | United Kingdom |
Start | 03/2016 |
End | 03/2017 |
Title | Carbon Data Model Framework - CARDAMOM |
Description | What is CARDAMOM? CARDAMOM (CARbon DAta MOdel fraMework) is a computer programme that retrieves terrestrial carbon (C) cycle variables by combining C cycle observations with a mass balance model. CARDAMOM produces global dynamic estimates of plant and soil C pools, their exchanges with each other and with the atmosphere, and C cycling variables for processes driving change. A Bayesian method is used to retrieve model parameters that statistically reduce the difference between model outputs and C observations for each model cell. The outcome is a probabilistic assessment of the fluxes, pools and process variables of the C cycle in each cell. Why is it useful? CARDAMOM produces a C cycle analysis consistent with C measurements and climate. It is well suited for using with global-scale satellite observations, for instance aboveground biomass or leaf area index. CARDAMOM produces a confidence interval on all retrieved quantities, so that there is a clear assessment of model-data consistency, and the value of assimilating extra datasets can be quantitatively assessed. Why is it novel? Conventional C cycle estimates rely on prescribed C cycle parameters; CARDAMOM retrieves parameters by combining models with data. The implementation of the CARDAMOM family of C models, (DALEC models), differs from typical model implementations: there is no spin-up for biomass and soil C, and no prescribed plant functional types or C pool steady state. CARDAMOM generates maps of key model parameters as an output, rather than as an input. Rather than being compared to data, the CARDAMOM outputs represent the best fit to all data within each CARDAMOM cell. How does it work? CARDAMOM has several components. First is a C mass balance model, DALEC, that has four vegetation pools and two dead organic matter pools (these pools can be adjusted to test alternate structures). There are around 20 model parameters associated with phenology (plant timing), allocation, residence times, temperature sensitivity and productivity. The second component is a system to arrange and organise spatial data related to forcing (climate, burned area, management) and to observations for assimilation (currently LAI, biomass, soil C). The third component is the Markov Chain Monte Carlo code, that operates on each model grid cell to retrieve model parameter vectors consistent with data. The fourth component is a structure to manage model-data interactions, distribute jobs to parallel computing nodes, retrieve state and process variables from optimized model parameters, and to summarise results for analysis. |
Type Of Material | Technology assay or reagent |
Year Produced | 2016 |
Provided To Others? | Yes |
Impact | We have generated publications, begun international partnerships, and been invited to speak about our research tool. |
Title | DALEC2 |
Description | Data assimilation linked ecosystem carbon model - an intermediate complexity model of the terrestrial carbon cycle. |
Type Of Material | Computer model/algorithm |
Year Produced | 2015 |
Provided To Others? | Yes |
Impact | DALEC is a core component of the CARDAMOM carbon-data model framework |
Title | Global GPP simulated by the JULES land surface model for 2001-2010 |
Description | This study evaluates the ability of the Joint UK Land Environment Simulator (JULES) Land Surface Model (LSM), the land surface scheme of the UK Met Office Unified Model (MetUM), to simulate Gross Primary Productivity (GPP) at regional and global scales for 2001--2010. Model simulations were driven with a variety of meteorological datasets and at various spatial resolutions (0.5x0.5, 1x1, 2x2 degree resolution). The meteorological datasets include: 1. WFDEI-GPCC The WATCH Forcing Data methodology was applied to the ERA-Interim reanalysis data (WFDEI) for the 1979--2012 period (Weedon et al., 2014. https://doi.org/10.1002/2014WR015638 ). WFDEI has two precipitation products, corrected using either CRU (Climate Research Unit at the University of East Anglia) or GPCC (Global Precipitation Climatology Centre) precipitation totals and are referred to as WFDEI-CRU and WFDEI-GPCC, respectively. 2. WFDEI-CRU See above. 3. PRINCETON The PRINCETON dataset is a global 62 year near-surface meteorological dataset used for driving land surface models and was created by Princeton University's Terrestrial Hydrology Group ( http://hydrology.princeton.edu/home.php ) (Sheffield et al., 2006. https://doi.org/10.1175/JCLI3790.1 ). JULES GPP was then compared to spatially gridded estimates of GPP from the upscaling of GPP from the FLUXNET network (FLUXNET-MTE), the MODIS sensor and the CARDAMOM framework. GPP, Gross Primary Productivity, is the total amount of carbon uptake by plants (per unit area in unit time) and used in photosynthesis. All files are in netCDF format. There is metadata in the files. To briefly view information regarding the files, there is software on Linux called ncdump (to read the data and metadata) and ncview (to plot the data without writing computer code). |
Type Of Material | Database/Collection of data |
Year Produced | 2016 |
Provided To Others? | Yes |
Impact | This study evaluates the ability of the JULES land surface model (LSM) to simulate photosynthesis using local and global data sets at 12 FLUXNET sites. Model param- eters include site-specific (local) values for each flux tower site and the default parameters used in the Hadley Cen- tre Global Environmental Model (HadGEM) climate model. Firstly, gross primary productivity (GPP) estimates from driving JULES with data derived from local site measure- ments were compared to observations from the FLUXNET network. When using local data, the model is biased with to- tal annual GPP underestimated by 16 % across all sites com- pared to observations. Secondly, GPP estimates from driving JULES with data derived from global parameter and atmo- spheric reanalysis (on scales of 100km or so) were com- pared to FLUXNET observations. It was found that model performance decreases further, with total annual GPP under- estimated by 30 % across all sites compared to observations. When JULES was driven using local parameters and global meteorological data, it was shown that global data could be used in place of FLUXNET data with a 7 % reduction in to- tal annual simulated GPP. Thirdly, the global meteorological data sets, WFDEI and PRINCETON, were compared to local data to find that the WFDEI data set more closely matches the local meteorological measurements (FLUXNET). Finally, the JULES phenology model was tested by comparing results from simulations using the default phenology model to those forced with the remote sensing product MODIS leaf area in- dex (LAI). Forcing the model with daily satellite LAI results in only small improvements in predicted GPP at a small num- ber of sites, compared to using the default phenology model. |
URL | https://www.geosci-model-dev.net/8/295/2015/gmd-8-295-2015.pdf |
Title | Reliability Ensemble Averaging of ISIMIP NPP projections for 2095-2099 under RCP8.5 |
Description | Multi-model averaging techniques provide opportunities to extract additional information from large ensembles of simulations. In particular, present-day model skill can be used to evaluate their potential performance in future climate simulations. Multi-model averaging methods have been used extensively in climate and hydrological sciences, but they have not been used to constrain projected plant productivity responses to climate change, which is a major uncertainty in earth system modelling. Here, we use three global observation-orientated estimates of current net primary productivity (NPP) to perform a reliability ensemble averaging (REA) using 30 global simulations of the 21st century change in NPP based on the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) 'business as usual' emissions scenario. |
Type Of Material | Database/Collection of data |
Year Produced | 2018 |
Provided To Others? | Yes |
Impact | We find that the three REAs support an increase in global NPP by the end of the 21st century (2095-2099) compared to 2001-2005, which is 2 - 3% stronger than the ensemble ISIMIP mean value of 24.2 Pg C y-1. Using REA also leads to a 45 - 68% reduction in the global uncertainty of 21st century NPP projection, which strengthens confidence in the resilience of the CO2 fertilization effect to climate change. This reduction in uncertainty is especially clear for boreal ecosystems although it may be an artefact due to the lack of representation of nutrient limitations on NPP in most models. Conversely, the large uncertainty that remains on the sign of the response of NPP in semi-arid regions points to the need for better observations and model development in these regions. |
URL | https://doi.org/10.5194/esd-2017-83 |
Title | Terrestrial carbon cycle reanalyses, 2000-2010 |
Description | The Carbon Data Model Framework generates robust estimates of terrestrial C cycling based on satellite observations, soil maps, climate data and a process model of C cycling. |
Type Of Material | Database/Collection of data |
Year Produced | 2016 |
Provided To Others? | Yes |
Impact | These data are associated with a high impact publication, and provide consistent C cycle estimates with errors across the globe. |
URL | http://datashare.is.ed.ac.uk/handle/10283/875 |
Description | Greenhouse Gas Summerschool |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Participants in your research and patient groups |
Results and Impact | Summerschool brought with 25 graduate students from around Europe to spend a week in Edinburgh. Students engaged in research, spoke at length with project staff, and shared their own research experiences. We generated a new network of young researchers to engage with on a range of greenhouse gas science topics. We also created new links among the project staff from the different domains (atmosphere, ocean, land) |
Year(s) Of Engagement Activity | 2015 |
URL | http://www.greenhouse-gases.org.uk/news/35-summer-school-2015 |
Description | Greenhouse Gas Townhall Meeting |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | The Greenhouse Gas (GHG) Emissions and Feedback Programme organised a 'Greenhouse Gas Townhall Meeting' at the National Oceanography Centre in Southampton on 7th-8th January 2015. We organised this meeting for the UK GHG scientific community to exchange ideas and identify concrete key needs for future greenhouse gas research in the UK, and to explore opportunities and developments in related national and international activities like ICOS. The meeting combined open science sessions with networking, workshops and breakout discussions as first steps towards developing community documents that can be submitted to the NERC SPAG and other relevant agencies. |
Year(s) Of Engagement Activity | 2015 |
URL | http://www.greenhouse-gases.org.uk/events/stakeholder-meeting-12-march-2014 |
Description | Greenhouse gases at the Edinburgh Science Festival |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | Yes |
Geographic Reach | National |
Primary Audience | Public/other audiences |
Results and Impact | We counted 3499 visitors to our presentations. There was considerable interaction with the public, who were able to take part in experiments, and discuss their findings with staff from the University of Edinburgh. We do not have any follow up activities to gauge the longer term impact of our presentations, but we are confident we generated interest in geosciences, and passed on detailed knowledge of greenhouse gases. |
Year(s) Of Engagement Activity | 2014 |
URL | http://www.greenhouse-gases.org.uk/blog/28-greenhouse-gases-at-the-edinburgh-science-festival |
Description | High school science engagement, Edinburgh |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Schools |
Results and Impact | The students worked with real scientific data, and our tutors engaged the students in the discovery process. The school teachers reported positive feedback from the students, and praised the opportunity provided. |
Year(s) Of Engagement Activity | 2015 |
Description | Our Dynamic Earth, EISF |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Public/other audiences |
Results and Impact | A debate on global change as part of the Edinburgh International Science Festival |
Year(s) Of Engagement Activity | 2017 |
Description | Stakeholder workshop for NERC Greenhouse gas programme |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Public/other audiences |
Results and Impact | We generated a report from the meeting, integrating the science outputs planned and achieved, and their relevance and value to stakeholders from government and business Since the meeting, we have developed further conversations with DECC and Defra, and with some of the businesses that attended. |
Year(s) Of Engagement Activity | 2014 |
URL | http://www.greenhouse-gases.org.uk/news/29-report-from-the-stakeholder-meeting-12-march-2014 |
Description | Talk at Royal Botanic Gardens Edinburgh symposium |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Policymakers/politicians |
Results and Impact | A talk on biodiversity from space to a broad audience from research, policy, NGOs and education |
Year(s) Of Engagement Activity | 2020 |
Description | Visit to INPE, Brazil |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Professional Practitioners |
Results and Impact | Research collaboration visit to INPE, Brazil to develop improved land surface models |
Year(s) Of Engagement Activity | 2019 |
Description | West Blackett Association Annual General Meeting |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | Local |
Primary Audience | Public/other audiences |
Results and Impact | A talk on 'Climate change and what to do about it' |
Year(s) Of Engagement Activity | 2017 |