GL: Primary Government Expenditures as % of Original Approved Budget data was reported at 99.673 % in 2012. This records a decrease from the previous number of 101.646 % for 2011. GL: Primary Government Expenditures as % of Original Approved Budget data is updated yearly, averaging 99.673 % from Dec 2010 (Median) to 2012, with 3 observations. The data reached an all-time high of 101.646 % in 2011 and a record low of 98.751 % in 2010. GL: Primary Government Expenditures as % of Original Approved Budget data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Greenland – Table GL.World Bank.WDI: Government Revenue, Expenditure and Finance. Primary government expenditures as a proportion of original approved budget measures the extent to which aggregate budget expenditure outturn reflects the amount originally approved, as defined in government budget documentation and fiscal reports. The coverage is budgetary central government (BCG) and the time period covered is the last three completed fiscal years.; ; Public Expenditure and Financial Accountability (PEFA). Ministry of Finance (MoF).; ;

This product includes all components of the surface energy budget at Summit, Greenland as well as some atmospheric drivers of variability in that budget over the period of 1/2011 to 6/2014. Individual energy budget terms include the upwelling and downwelling longwave and shortwave radiation, turbulent sensible heat flux, turbulent latent heat flux, and sub-surface heat flux. Supporting measurements include near surface meteorology and cloud liquid water path. Raw measurements used to determine these various parameters were made by the NSF ICECAPS project (PIs: Von Walden, Matthew Shupe, David Turner, Ralf Bennartz), the NSF CIBS project (PI: David Noone), ETH (PI: Koni Steffen), and NOAA at Summit, Greenland.

CLIMATE APPLICATIONS: Global sea-level budget, Antarctic mass balance interpretation, Greenland mass balance interpretation. Products from CCI are not publicly released yet (due Apr 2017). Similar data was used in McMillan et al, 2014. GRL, Increased ice losses from Antarctica detected by CryoSat-2 doi:10.1002/2014GL060111

This dataset is a compilation of time series, together with uncertainties, of the following elements of the global mean sea level budget and ocean mass budget: (a) global mean sea level (b) the steric contribution to global mean sea level, that is, the effect of ocean water density change, which is dominated, on a global average, by thermal expansion (c) the mass contribution to global mean sea level (d) the global glaciers contribution (excluding Greenland and Antarctica) (e) the Greenland Ice Sheet and Greenland peripheral glaciers contribution (f) the Antarctic Ice Sheet contribution (g) the contribution from changes in land water storage (including snow cover). The compilation is a result from the Sea-level Budget Closure (SLBC_cci) project conducted in the framework of ESA’s Climate Change Initiative (CCI). It provides assessments of the global mean sea level and ocean mass budgets. Assessment of the global mean sea level budget means to assess how well (a) agrees, within uncertainties, to the sum of (b) and (c) or to the sum of (b), (d), (e), (f) and (g). Assessment of the ocean mass budget means to assess how well (c) agrees to the sum (d), (e), (f) and (g). All time series are expressed in terms of anomalies (in millimetres of equivalent global mean sea level) with respect to the mean value over the 10-year reference period 2006-2015. The temporal resolution is monthly. The temporal range is from January 1993 to December 2016. Some time series do not cover this full temporal range. All time series are complete over the temporal range from January 2003 to August 2016. For some elements, more than one time series are given, as a result of different assessments from different data sources and methods. Data and methods underlying the time series are as follows: (a) satellite altimetry analysis by the Sea Level CCI project. (b) a new analysis of Argo drifter data with incorporation of sea surface temperature data; an alternative time series consists in an ensemble mean over previous global mean steric sea level anomaly time series. (c) analysis of monthly global gravity field solutions from the Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry mission. (d) results from a global glacier model. (e) analysis of satellite radar altimetry over the Greenland Ice Sheet, amended by results from the global glacier model for the Greenland peripheral glaciers; an alternative time series consists of results from GRACE satellite gravimetry. (f) analysis of satellite radar altimetry over the Antarctic Ice Sheet; an alternative time series consists of results from GRACE satellite gravimetry. (g) results from the WaterGAP global hydrological model. Version 2.2 is an update of the previous Version 2.1. The update concerns the estimates of ocean mass change from GRACE.

Information on development assistance financed through the EU budget in Greenland. The Directorate-General for International Partnerships is responsible for formulating the EU’s international partnership and development policy, with the ultimate goal to reduce poverty, ensure sustainable development, and promote democracy, human rights, and the rule of law across the world. Its main purpose is to implement most of the European Commission’s external assistance, through instruments such as the the Neighborhood, Development and International Cooperation Instrument (NDICI). This vital task requires DG INTPA to deliver the EU’s development assistance budget in an open and accountable fashion. For this reason, it publishes its data in the International Aid Transparency Initiative (IATI) Registry since 2011.

This dataset is a compilation of time series, together with uncertainties, of the following elements of the global mean sea level budget and ocean mass budget: (a) global mean sea level (b) the steric contribution to global mean sea level, that is, the effect of ocean water density change, which is dominated, on a global average, by thermal expansion (c) the mass contribution to global mean sea level (d) the global glaciers contribution (excluding Greenland and Antarctica) (e) the Greenland Ice Sheet and Greenland peripheral glaciers contribution (f) the Antarctic Ice Sheet contribution (g) the contribution from changes in land water storage (including snow cover).

The compilation is a result from the Sea-level Budget Closure (SLBC_cci) project conducted in the framework of ESA’s Climate Change Initiative (CCI). It provides assessments of the global mean sea level and ocean mass budgets. Assessment of the global mean sea level budget means to assess how well (a) agrees, within uncertainties, to the sum of (b) and (c) or to the sum of (b), (d), (e), (f) and (g). Assessment of the ocean mass budget means to assess how well (c) agrees to the sum (d), (e), (f) and (g).

All time series are expressed in terms of anomalies (in millimetres of equivalent global mean sea level) with respect to the mean value over the 10-year reference period 2006-2015. The temporal resolution is monthly. The temporal range is from January 1993 to December 2016. Some time series do not cover this full temporal range. All time series are complete over the temporal range from January 2003 to August 2016.

For some elements, more than one time series are given, as a result of different assessments from different data sources and methods.

Data and methods underlying the time series are as follows: (a) satellite altimetry analysis by the Sea Level CCI project. (b) a new analysis of Argo drifter data with incorporation of sea surface temperature data; an alternative time series consists in an ensemble mean over previous global mean steric sea level anomaly time series. (c) analysis of monthly global gravity field solutions from the Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry mission. (d) results from a global glacier model. (e) analysis of satellite radar altimetry over the Greenland Ice Sheet, amended by results from the global glacier model for the Greenland peripheral glaciers; an alternative time series consists of results from GRACE satellite gravimetry. (f) analysis of satellite radar altimetry over the Antarctic Ice Sheet; an alternative time series consists of results from GRACE satellite gravimetry. (g) results from the WaterGAP global hydrological model.

GL: Primary Government Expenditures as % of Original Approved Budget在2012达99.673 %，相较于2011的101.646 %有所下降。GL: Primary Government Expenditures as % of Original Approved Budget数据按每年更新，2010至2012期间平均值为99.673 %，共3份观测结果。该数据的历史最高值出现于2011，达101.646 %，而历史最低值则出现于2010，为98.751 %。CEIC提供的GL: Primary Government Expenditures as % of Original Approved Budget数据处于定期更新的状态，数据来源于World Bank，数据归类于Global Database的格陵兰岛 – Table GL.World Bank.WDI: Government Revenue, Expenditure and Finance。

CLIMATE APPLICATIONS: Global sea-level budget, Greenland mass balance interpretation e.g. for Greenland and Antarctica: - A Reconciled Estimate of Ice-Sheet Mass Balance, Andrew Shepherd et al, Science 30 Nov 2012: Vol. 338, Issue 6111, pp. 1183-1189 DOI: 10.1126/science.1228102 For Antarctica only: Increased Ice Losses from Antarctica Detected by CryoSat-2, Malcolm McMillan et al, GRL, Volume 41, Issue 11, 16 June 2014, Pages 3899�3905, DOI: 10.1002/2014GL060111

Funds are provided to characterize the interactions among the atmospheric state, cloud properties, radiation, and precipitation at Summit, Greenland. The objective is to investigate a number of important cloud-related processes, how these interact with the Arctic climate system, and their impact on the surface energy and mass budgets. Specific foci will include:

1) Low-cloud persistence mechanisms that lead to long-lived Arctic stratiform clouds, which interact strongly with the atmospheric structure and surface energy budget;

2) Cloud-phase partitioning, which determines the cloud microphysical composition and, ultimately, the effects that clouds have on atmospheric radiation and the hydrologic cycle; and

3) Precipitation partitioning, in order to understand the different modes of precipitation at Summit and how these impact the total surface accumulation.

To address these topics, this project will utilize detailed observations from a suite of ground-based remote sensors deployed at Summit as part of the NSF/AON-funded ICECAPS project in combination with data from satellite-borne active remote sensors. High-resolution numerical modeling will also be used to investigate many of the fine-scale cloud processes and their mesoscale influences. These studies over the Greenland Ice Sheet will also be considered within the context of similar measurements and model studies made at other Arctic locations in order to understand these important processes over Summit and, in a more general sense, across the Arctic.

Atmospheric water vapor, clouds, and precipitation greatly affect the surface energy and cryospheric mass balances in the Arctic, and are responsible for much of the variability in these balances. It is thought that recent rapid melting of Arctic sea ice may be driven, in part, by changes in cloud cover and radiation. Cloud-related processes and feedbacks are known to be one of the greatest sources of uncertainty in global climate models, and these shortcomings have been clearly identified in model simulations over the Arctic. Thus, the results of this project should improve our understanding of Arctic cloud processes and their inclusion in climate models, which, in turn, will improve predictability.

As broader impacts of this research, the project will provide important data analysis and integration experience for four new graduate students at the participating universities. In addition, data and results from this study will be integrated into undergraduate coursework and summer workshops for high school students and teachers.

Methane concentrations (μmol L-1) across the study region in southwest Greenland.

This project will perform diagnostic analyses of the processes modulating the surface radiative, turbulent, and conductive fluxes at several Study of Environmental Arctic Change (SEARCH) climate observatories located around the Arctic Ocean in Canada, Alaska, and Siberia to investigate the annual cycle of the surface energy budget (SEB) and its coupling to atmospheric and surface processes. Where necessary, existing observations will be augmented to complete the suite of SEB measurements. Data exist or will be obtained to focus on the following scientific questions: (i) What processes govern the SEB at Arctic terrestrial sites? What role do local effects such as terrain or coastlines play? How large is the local spatial SEB heterogeneity? How do the physical processes affecting the SEB differ among the various sites? How do these SEB climatologies compare with a sea-ice regime as represented by the SHEBA site, or with that of Greenland? Which SEB terms might be impacted by climate change and how? (ii) What is the relative contribution from classical Monin-Obukhov similarity (MOS) and non-MOS processes to heat and momentum fluxes at Arctic terrestrial sites? Are existing bulk algorithms for surface turbulent fluxes in models applicable at Arctic sites or is the development of new ones necessary? (iii) Which SEB terms determine the soil temperatures and the active layer depth? What mechanisms force variability in those terms? How does the annual cycle of snow cover at each site influence the SEB and thus temperature regimes? Comparisons of key processes at these terrestrial sites will be made to those done by other researchers over Arctic sea ice and on Greenland. These coordinated observations and analyses, which provide process understanding of atmospheric-soil interactions in the Arctic, are rare and will be of interest to a broad spectrum of the scientific community, including the remote sensing and modeling communities. The resulting data and analyses will likely be key data sources for future model studies in a variety of disciplines. Physical understanding of the modulation of energy fluxes to permafrost should provide enhanced understanding of the potential for the greenhouse gas release process in climate-change scenarios. The project takes advantage of interagency and international collaborations with investigators located around the Arctic (USA, Canada, and Russia) and will contribute to education on Arctic climate systems through partnership with the CIRES Education and Outreach group, leading to teacher development and classroom implementation of new climate topics.

In the North Atlantic, there are two main western boundary currents related to the Atlantic Meridional Overturning Circulation (AMOC): the Gulf Stream flowing northward and the Deep Western Boundary Current (DWBC) flowing southward. Here we analyze data from the OVIDE section (GO-SHIP A25 Portugal-Greenland 40–60°N) that crosses the DWBC and the northward extension of the Gulf Stream, the North Atlantic Current. We show that North Atlantic western boundary currents play a key role in the transport of dissolved organic matter, specifically dissolved organic carbon (DOC). Revisited transports and budgets of DOC with new available data identify the eastern Subpolar North Atlantic (eSPNA) as an important source of locally produced organic matter for the North Atlantic and a key region in the supply of bioavailable DOC to the deep ocean. The East Greenland Current, and its upstream source the East Reykjanes Ridge Current on the eastern flank of the mid-Atlantic ridge, are export pathways of bioavailable DOC toward subtropical latitudes. The fast overturning and subsequent remineralization of DOC produced in the autotrophic eSPNA explains up to 38% of the total oxygen consumption in the deep North Atlantic between the OVIDE section and 24°N. Carbon budgets that do not take into account this organic remineralization process overestimates the natural uptake of carbon dioxide (CO2) from the atmosphere by one third. The inclusion of DOC transports in regional carbon budgets reconciles the estimates of CO2 uptake in the North Atlantic between model and observations.

Regression results for predicting methane (CH4) in southwest Greenland lakes, along with the validation of this model using 2013 pilot lake data.

Lake characteristics for pilot lakes sampled in 2013.