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EUropean Reanalysis and Observations for Monitoring

Final Report Summary - EURO4M (EUropean Reanalysis and Observations for Monitoring)

Executive Summary:
The goal of EURO4M was to meet the need for timely and reliable monitoring information about the state of the climate in Europe. User consultations at the start of the project indicated that a clear need exists for long-term high-resolution data, and particularly also for value-added data products which describe the evolution of the physical climate system on a European scale.
In the past four years EURO4M has developed regional reanalyses of past weather and user-oriented data products for monitoring climate variability and change in Europe. The project addressed the situation of fragmentation and scarcity of long-term climate change monitoring information. It did so by combining newly developed comprehensive datasets from model-based regional reanalyses with improved Essential Climate Variable (ECV) datasets from satellites and ground-based in situ observations.
In short: EURO4M has helped to develop the capacity for delivering the best possible and most complete (gridded) climate change time series and monitoring services covering all of Europe.

Who was involved?

EURO4M was a collaborative effort of 9 European partners. The Management Board was made up of the following Principal Investigators: Phil Jones (Climatic Research, University of East Anglia, United Kingdom), Dale Barker (Met Office, United Kingdom), Roxana Bojariu (National Meteorological Administration, Romania) and Albert Klein Tank (Royal Netherlands Meteorological Institute, Project Coordinator).
The Principal Investigators for the other project partners were: Manola Brunet (University Rovira i Virgili, Spain), Christoph Frei (Meteo Swiss, Switzerland), Richard Müller / Jörg Trentmann (Deutscher Wetterdienst, Germany), Per Unden (Swedish Meteorological and Hydrological Institute, Sweden), and Eric Bazile (Météo France, France).
The EU Project Officer was Stijn Vermoote (REA, Belgium). The Advisory Board of EURO4M consisted of: Velina Pendolovska (DG CLIMA, Belgium), Andre Jol / Blaz Kurnik (EEA, Denmark), and Adrian Simmons, ECMWF, United Kingdom.
The Project Office at KNMI (The Netherlands), which was responsible for the routine administration of the project and the scientific direction, was staffed by Karin van der Schaft, Gé Verver and Albert Klein Tank.

Project Context and Objectives:
As the primary source of timely, targeted and reliable information about the state of the climate in Europe, the suggested collaborative project is an important building block for the Global Monitoring for Environment and Security (GMES) initiative. No other coordinated contribution for this area exists or is currently planned within GMES. The current GMES services, which have already entered into their pre-operational phase, are not designed to provide climate change monitoring information nor reports about high impact weather and climate extremes placed in an historical context. For example, the Atmosphere service is mainly directed towards air quality and focuses on the shorter time scales. Also, the in situ component of GMES at present does not fully address meteorological observations. The coordination action for in situ data indicated in the Work Programme alone will not result in comprehensive pan-European climate datasets at a useful level of aggregation and processing.

Climate change is the societal benefit area of the Group on Earth Observations that lacks, and urgently needs, an integrated and coordinated approach with a focus, in particular, on climate information for the multi-decadal time scales that are most relevant for adaptation. The other societal benefit areas (water, natural and human-induced disasters, environment and health, energy, ecosystem services, agriculture and desertification, biodiversity) are already reasonably well covered in the existing data information systems, such as the European Environment Information and Observation NETwork (EIONET) and the Shared Environmental Information System (SEIS). Integrated long-term and high-quality datasets of climate change information (in particular extremes) in terms of atmospheric Essential Climate Variables (ECVs) are typically missing (see dataservice.eea.europa.eu).
This situation is limiting the response strategies to adapt to climate variability and change at the regional, sub-regional and national scales. In particular, information on changes in weather and climate extremes is crucial, especially as the driving force for the impact work in all GMES services and GEO areas. If the relevant climate change information is not made available, then these services and areas will not be able to be successful. Due to a lack of coherent information on weather and climate extremes many GMES services and GEO areas base their work on changes in mean climate only. However, it is generally accepted that the impacts of climate change are caused primarily by changes in variability and extremes, rather than changes in the mean climate. For adaptation strategies, the longer (multi-decadal) time scales are particularly relevant, because nearly all infrastructure design relies on assessment of probabilities of extremes with return periods of 50 years. These assessments should take into account that the climate is non stationary because of climate change. It is the longer time scale that is needed for governments to implement their climate change action plans.
The members of this project’s consortium are currently the main source of climate change time series and monitoring information for governments, policy-makers and the general public across Europe. They are frequently approached by the European Environment Agency (EEA), Joint Research Centre (JRC), World Meteorological Organization (WMO) and World Health Organization (WHO) to contribute this information to environmental assessment reports. The EURO4M beneficiaries MO and UEA collectively provide information on European temperature change over the past decades based on their global datasets, which are also prime inputs to the reports of the Intergovernmental Panel on Climate Change (IPCC) and to the Conference of the Parties to the United Nations Framework Convention on Climate Change (UNFCCC). KNMI contributes up-to-date information about trends in extremes by linking the historical data archives of more than 40 meteorological services and universities in Europe through the European Climate Assessment & Dataset project (ECA&D; a forthcoming WMO Regional Climate Centre). DWD operates the Global Precipitation Climatology Centre (GPCC), which provides global analysis of precipitation on the earth’s land surface based on in situ rain gauge data.
All these activities have an ad hoc character and limited spatial (horizontal and vertical) resolution for the longer time scales. Also, the potential of data assimilation and reanalysis for climate change monitoring are not yet fully exploited. MO, SMHI and MF are at the forefront of data assimilation developments in Europe, but no regional reanalysis for Europe is currently available that improves on the existing global reanalyses (particularly ERA-40 at the European Centre for Medium-Range Weather Forecasts ECMWF). URV, NMA-RO and MS have contributed significantly to improved observational datasets for sub-regions, but these activities have not yet been integrated within the European context. DWD coordinates the Satellite Application Facility on Climate Monitoring (CM-SAF) of EUMETSAT, which aims at the provision of satellite-derived geophysical parameter datasets suitable for climate monitoring. These datasets need integration too.
In summary, for GMES to become a success, the situation of fragmentation and scarcity of long-term climate change monitoring information in Europe needs to change. There is the vast task of integrating national observing systems, existing global and European observation datasets, satellite-derived datasets and reanalyses into GMES (see Butler, 2007). This is needed to fill the gap for surface climatological data and information which, at present, is clearly visible in all environmental assessments. In the words of our third party participant EEA: “Everybody is expecting that weather and climate data is simply available (according to their experience having weather forecasts for every location on every day), but for historical data on climate extremes this is clearly not the case”.

Objective(s)
The overall goal of EURO4M is to develop the capacity for, and deliver the best possible and most complete (gridded) climate change time series and monitoring services covering all of Europe. These will enable adequate descriptions of the status and evolution of the Earth system components.
Specifically, the objectives of EURO4M are to:
1. Generate time series of observation datasets and reanalyses of past observational data;
• build on and integrate existing European in situ and satellite datasets, bearing in mind global connectivity,
interoperability and data sharing;
• develop the capacity for climate quality dynamic reanalysis that optimally integrates the widest possible range
of in situ and satellite data;
• demonstrate the capability of regional reanalysis and multi-staged downscaling with increased levels of
accuracy;
• deliver demonstration regional reanalysis for parts of the past 20 years;
• reduce gaps and deficiencies in European monitoring capacity through a better exploitation of existing
atmospheric observations and data exchange;
• build on the strong synergy the beneficiaries have with other major climate monitoring centres worldwide, in
particular with centres involved in global reanalysis.
2. Produce innovative and integrated high-quality data products for research and applications sector users;
• produce multi-purpose products and information to assist climate change research to incorporate the
monitored ECVs;
• provide reliable, up-to-date scientific input (especially through the IPCC) for the implementation of European
and international policies and strategies on the environment and society, including the EU climate adaptation
strategy;
• provide online reporting during emerging extreme events.

3. Reach out with data products and climate change services to the user community, stakeholders, policy-makers, and general public;
• demonstrate the climate change services to policy-makers, researchers, planners and citizens at European,
national and local levels;
• hold frequent dialogues and interactions with a wide range of end-users to achieve a better understanding of
information needs and formats;
• make the data and information readily accessible to users with full consideration of the appropriate level of
aggregation and standardization.

4. Evolve into a future GMES service on climate change monitoring that is fully complimentary and supporting
the existing core services.
• integrate and extend core GMES services activities on ECVs, specifically developing the capacity required for
user-oriented multi-purpose products for monitoring of climate change;
• link to existing GMES services, and especially those on marine, land and atmosphere monitoring, which
include – or will include in the near future – a global component by design;
• stimulate the GMES downstream sector;
• demonstrate and strengthen the European leadership in long-term monitoring of climate change.
These objectives will be achieved over a 4 yr period in 4 major Work Packages (WPs). Together, they comprehensively address the scientific, technical and wider societal and policy objectives of the Sub-activity SPA.2009.1.1.02 “Monitoring of climate change issues (extending core service activities)”.

Project Results:
Deliverables part A: reference historical databases

New regional reanalyses at 12-25 km resolution were produced as part of the project using the state-of-the-art regional weather models HIRLAM and NAE. The great benefit of a reanalysis is that it provides a complete picture of the atmosphere, covering the whole of the 3-dimensional domain (including all ECVs), not only of the observed variables, but also of those that are not directly measured.
In order to further enhance the 12-25 km resolution of the regional reanalyses to the local scale, 2-dimensional downscaling was performed using the systems MESAN and MESCAN. These high-resolution analysis systems employ regional variations given by observational statistics and physiographic factors such as land-sea mask and orography. This results in downscaled gridded climate time series of surface variables at about 5 km resolution.
Due to computational constraints and input data limitations, the regional reanalyses and downscaled datasets typically cover a time period from a few up to 20 years. For climate change applications in risk management and science-based adaptation most users need information about longer-term changes. In particular, information about climate trends and changing probabilities of high impact extremes (such as flooding or heat waves) cannot be derived from relatively short reanalyses datasets only. Therefore, the reanalyses were combined with multi-decadal satellite datasets and century-scale in situ observations. The satellite data also provide the higher spatial resolution which is sometimes required.
Long-term gridded climate time series based on satellite data and interpolated station observation datasets developed as part of the project include the updated and extended versions of the monthly GPCC precipitation dataset, the monthly CRU temperature, precipitation and humidity datasets, the daily ECA&D datasets of multiple ECVs, the daily Alpine precipitation dataset of MS and the daily gridded climate datasets for Romania of NMA-RO.
Within EURO4M, the European domain consists of the area 25W-45E and 30N-75N (minimum required) or 30W-60E and 30N-85N (desired).

Deliverables part B: Climate Indicator Bulletins (CIBs)

The reference historical databases developed within the project enabled that observed high-impact weather and climate extremes were placed in a long-term historical context. To guide this process, so-called Climate Indicator Bulletins (CIBs) have been developed which consist of knowledge abstractions from different datasets including associated uncertainty estimates. By integrating the different data sources, these bulletins respond to user demands and improve the climate change services for European society.

Data rescue and digitization

Significant efforts to further digitise remaining climate records for the Mediterranean from various archives have been undertaken. Ancient parts of the records recovered under EURO4M have been merged with present-day parts gathered from different digital data sources. The merged time-series have been subjected to a homogenisation exercises by applying two state-of-the-art homogenisation methods (ACMANT and HOMER).

Improved capabilities for regional reanalysis

The model-based European regional reanalysis capabilities have been further developed and reanalysis datasets have been delivered. The results demonstrate that regional reanalysis improves the representation of variables necessary for climate monitoring. In particular regional reanalysis can improve representation of precipitation. High resolution and sophisticated assimilation techniques are necessary to best represent high threshold events which cause the greatest damage and economic impact. The improved regional reanalysis capabilities give a head-start to the EURO4M follow-on project Uncertainties in Ensembles of Regional ReAnalyses (UERRA). See below.

Evaluation of data quality

Climate datasets derived from different sources (model reanlysis, satellites or ground-based observations) differ in spatial and temporal resolution and in the time period for which data is available. No matter how thoroughly climate datasets are constructed, they are all afflicted with uncertainties. Reasons for this are limitations in the coverage and representativity of surface observations, ambiguities in the inference from remote-sensing measurements, and simplifications made in the modelling of atmospheric processes. Inaccuracies and uncertainties in the datasets may be of concern in applications. Therefore, EURO4M has made evaluations and comparisons that inform users about the potential and limitations of the new datasets as well as characteristic differences between them.
The comparisons also give insight to the producers of the datasets on the benefit from certain methodologies and processing steps. They dealt with quantifying uncertainties and describing error patterns of the various datasets and gaining insight into the benefit from particular methodological developments in their construction. To this end results from the regional reanalyses and downscaling datasets were compared to station observations, interpolation datasets, satellite data and the global reanalysis (ERA-interim) that was used for driving the regional reanalyses.
The results from these evaluation activities have been summarized in a synthesis report that also points towards some general implications for potential users of the new EURO4M datasets.

Potential Impact:
Contributions to the future Copernicus Climate Change service

The EURO4M project has extended, in a cost effective manner, European capacity to systematically monitor climate variability and change (including extremes) on a range of space and time scales. EURO4M reached out with innovative and integrated data products and climate change services to policy-makers, researchers, planners and citizens at European, national and local levels. The project has directly addressed the needs of, for instance, the European Environment Agency for their environmental assessment reports.
As a primary source of timely, targeted and reliable information about the state of the climate in Europe, EURO4M is an important building block for Copernicus. The project integrates and extends core services activities on ECVs, also by developing the capacity required for state-of-the-art user-oriented products for monitoring climate change.
The table below provides an overview of all datasets developed as part of EURO4M. This table is part of a webpage with pointers to all datasets developed as part of the project plus fact sheets describing the main characteristics (see: http://www.euro4m.eu/datasets.html).
Note that the EURO4M project did not intend to provide a single data archive for all data, nor did we aim to integrate the various sources in existing international archives. Also, fully unified visualisation tools are not part of the project although a pilot visualisation tool has been developed and populated with a selection of datasets that can be displayed (see: http://euro4mvis.knmi.nl). The observation datasets and the reanalyses datasets of several atmospheric ECV's which are available for visualisation are marked in the table by a star.



Essential Climate Variable
-- Datasets Res.
Inst. Area Spatial
Res. Temporal
Res. Period Format Reference
1* Precipitation
Alpine precipitation grid dataset (EURO4M-APGD)(D1.1)
MS European Alps and adjacent flatland 5 km daily 1971-2008 NedCDF Factsheet: Alpine Precipitation Grid Dataset.

Isotta, F.A. et al., 2013: The climate of daily
precipitation in the Alps: development and analysis of a high-resolution grid dataset from pan-Alpine rain-gauge data. Int. J. Climatol., 34 (5), 1657-1675.

2* Atmospheric surface variables
European Climate Assessment & Dataset (ECA&D).
KNMI Europea, North Africa and the Middle East point data daily 1775-present ASCII Factsheet: Daily station data - ECA&D (European Climate Assessment & Dataset).

Klein Tank, A.M.G. and Coauthors, 2002. Daily
dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int. J. of Climatol., 22, 1441-1453.

3 Air temperature, pressure, precipitation
Updated and merged Mediterranean station dataset (D1.12 D1.13)
URV All countries bordering the Mediterranean Sea point data daily 1850-1970 ASCII Factsheet: Merged climate dataset for the Mediterranean.

4 Air temp., Surface soil temperature, Precipitation, Sea level pressure, Cloud cover, Sunshine duration, Relative humidity
Daily gridded datasets Romania
NMA-RO Romania 0.10 x 0.10 degree daily 1961-01-01 to 2013-12-31 netCDF Factsheet: Daily gridded datasets Romania.

5* Air temperature, pressure, precipitation
E-OBS gridded dataset (D1.4).
KNMI Europea, North Africa and the Middle East 25 km or 50 km daily 1950-present NetCDF Factsheet: E-OBS gridded dataset (D1.4).

Haylock, M.R. N. Hofstra, A.M.G. Klein Tank, E.J. Klok, P.D. Jones and M. New. 2008: A European daily high-resolution gridded dataset of surface temperature and precipitation. J. Geophys. Res (Atmospheres), 113, D20119, doi:10.1029/2008JD10201

Besselaar, E.J.M. van den, M.R. Haylock, A.M.G. Klein Tank en G. van der Schrier, A European Daily High-resolution Observational Gridded Data set of Sea Level Pressure J. Geophys. Res., 2011, 116, D11110, doi:10.1029/2010JD015468

6* Air temperature, pressure, precipitation, water vapour
CRU/UEA gridded data products (D1.6).
UEA Global (European window available) 0.5 degree monthly 1901-2011 ASCII Factsheet: CRU/UEA Data Products

7 Precipitation
Global Precipitation Climatology Centre (GPCC) full data reanalysis version 5 (D1.3)
DWD Global (European window available) 0.5 degree monthly 1901-2009 ASCII Becker, A. et al., 2013: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901-present. Earth Syst. Sci. Data, 5, 71-99, doi:10.5194/essd-5-71-2013

8* Precipitation
Integrated HOAPS/GPCC precipitation gridded dataset (D1.8).
DWD Global (European window available) 0.5 degree monthly 1987-2008 NetCDF Factsheet:Integrated HOAPS/GPCC precipitation data.

9* Surface radiation budget
Surface solar irradiance (SIS) gridded dataset (D1.7).
DWD Europe, NOrth Africa and the Middle East 0.03 degree monthly 1983-2005 NetCDF Factsheet: Surface solar irradiance (SIS).

10* Water vapour
ATOVS water vapour gridded dataset (D1.9).
DWD Global (European window available) 90 km monthly 1999-2012 NetCDF Factsheet: ATOVS-based gridded dataset of Integrated Water Vapor.

11 Surface radiation, precipitation, cloud properties
MSG-based gridded datasets of clouds, precipitation and radiation (D1.10).
KNMI Europe, incl. North Africa 3 km 15 minutes 2005-present NetCDF Factsheet: High-resolution datasets of precipitation, SSI and cloud properties for the MSG period (2005-present).

12 - Daily surface min. and max. temperature,

- Daily and monthly sunshine duration
SEVIRI-based gridded datasets of Temperature and sunshine duration

(D1.11). Met Office Europe ~4-10 km Temperature:
daily


Sunshine:
15 minutes Temperature: 2012-2013


Sunshine:
2009 - early 2012 NetCDF Factsheet: SEVIRI Daily Minimum and Maximum Surface Temperature.

Factsheet: SEVIRI Daily and Monthly Sunshine Duration.

Report: Daily Minimum and Maximum Surface Temperatures from SEVIRI.

Good, E., Estimating daily sunshine duration over the UK from geo-stationary satellite dataVIRI Daily Minimum and Maximum Surface Temperature, Weather, December 2010, Vol. 65, No. 12, 324-328. doi: 10.1002/wea.619

Kothe, S., Good, E., Obregón, A., Ahrens, B., Nitsche, H., 2013, Satellite-Based Sunshine Duration for Europe. Remote Sens. 2013, 5, 2943-2972; doi:10.3390/rs5062943

13* Atmospheric surface and upper-air variables
4D-VAR regional reanalysis (D2.1)
Met Office Europe, North Africa and the Middle East 12 km monthly 2008-2009 NetCDF Factsheet: Met Office 4D-VAR Reanalysis.

14* Atmospheric surface and upper-air variables
3D-VAR regional reanalysis (D2.3)
SMHI Europe, North Africa and the Middle East 0.2 degree 6 hourly 1989-2010 GRIB Factsheet: HIRLAM 3D-VAR dynamical downscaling re-analysis.

15 Atmospheric surface variables
MESAN 2D surface downscaling reanalysis (D2.4)
SMHI Europe, North Africa and the Middle East 0.05 degree daily 1989-1997 GRIB Factsheet: MESAN 2D surface downscaling re-analysis.

16 Atmospheric surface variables
MESCAN 2D surface downscaling reanalysis (D2.6)
MF Europe 0.05 degree 6 hourly 2007-2010 netCDF Factsheet: Surface downscaling reanalysis.




Below are comments on the readiness for operational delivery of each EURO4M dataset after the closing date of the project (31 March 2014).

dataset participant readiness for operational delivery
1* Alpine precipitation gridded dataset MS Continued dissemination of existing dataset via:
http://www.meteoschweiz.admin.ch/web/de/services/datenportal/gitterdaten/alpineprecip.html
The possibility for a regular update (monthly resolution only, reconstruction from key stations) is elaborated in the course of UERRA.
2* ECA&D station dataset KNMI Updated on a monthly basis using KNMI and EUMETNET funds (which will end in 2015). Disseminated from the dedicated ECA&D website www.ecad.eu

3 Mediterranean station dataset URV Both the Mediterranean historical climate observations (publicly available from https://zenodo.org/record/7531#.U3DX0ShjZVI) and the merged and homogenised climate time-series (accessible from the ECA&D portal, see 3) are fully operational products ready to be used. Neither the observations nor the climate time-series need updates, because they refer to the outcome of the data rescue activity of EURO4M.
4 Daily gridded dataset for Romania NMA-RO Dataset will be maintained and elaborated for future extensions in space and time as part of UERRA. Disseminated from https://sites.google.com/site/euro4mdata/file-cabinet

5* E-OBS gridded dataset KNMI Updated on a monthly (and soon daily) basis as part of UERRA. Disseminated from www.ecad.eu

6* CRU/UEA data products UEA Updated on a 6-monthly schedule by the British Atmospheric Data Centre (BADC) in the UK, using software provided by CRU. The updates occur in the spring (to complete the previous year) and in the autumn (to complete the grids for the first six months of the year). The delay behind real time is to ensure that one of the principal sources of input data (Monthly Climatic Data for the World, MCDW, produced by NCDC) is available. This update produces all the gridded datasets discussed in Harris et al. (2014). The scPDSI dataset is currently updated by KNMI once the precipitation and PET grids are available.
7 GPCC precipitation gridded dataset DWD The data set of land surface precipitation is regularly updated as part of the Global Precipitation Climatology Centre (GPCC, gpcc.dwd.de) of WMO.
8* Integrated HOAPS/GPCC dataset DWD The data set of land surface precipitation is regularly updated as part of the Global Precipitation Climatology Centre (GPCC, gpcc.dwd.de) of WMO.
9* Solar radiation gridded datasets DWD Updated data set (covering 1983 to 2013, available in Fall 2014) and operational surface radiation data are available from the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF, www.cmsaf.eu).
10* ATOVS water vapour gridded dataset DWD Updated and operational water vapour data are available from the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF, www.cmsaf.eu).
11 MSG-based datasets KNMI Updated in near-real time using KNMI funds. Disseminated from http://msgcpp.knmi.nl

12 SEVIRI-based datasets MO The daily LSTmin and LSTmax data sets (land ‘skin’ temperatures) are based on 15-minute satellite data and can be made available one day after aquisition. The satellite daily Tmin and Tmax data can be updated in line with ECA&D (see 2), within a few days of the release of new station data. Continued production of the surface temperature products will be funded by the UK government. The satellite sunshine duration data set is currently a static data set but should soon be available in real time with a one-day time lag.
13* 4D-VAR regional reanalysis dataset MO Pilot reanalysis dataset not intended to be updated. Newer and improved versions will be developed as part of UERRA. These will have the potential to become operational services.
14* 3D-VAR regional reanalysis dataset SMHI The comprehensive regional 3-dimensional reanalysis over the European-Atlantic at 22 km and 40 levels up to 30 km is available for 1989 – 2010, and will be extended in time as part of UERRA. Available on request from SMHI (www.smhi.se) and a future data service is built within the FP7-project CLIPC.
15 MESAN 2D downscaling dataset SMHI The 2-dimensional 5 km downscaling analysis from the 3-d reanalysis above (dataset 14*) and using additional surface observations. This dataset for 2 m temperature, humidity and precipitation for the 20 year period above and 10 m winds for parts of the period. Data can be extracted by SMHI and within the future data service in CLIPC.
16 MESCAN 2D downscaling dataset MF The 4 years data (2007-2010) for the high resolution 2D downscaling produced in EURO4M are a first version of the MESCAN surface re-analysis. An updated and improved version will be provided in the UERRA project for a period of 50 years.


Operational CIB delivery

Climate Indicator Bulletins (CIBs) based on the datasets above have been developed using a Mediawiki with embedded Web Mapping Services. These services are similar to the pilot visualisation tool described above. This tool will be used and further developed in UERRA. The tool is adequate for future operational use because it follows open standards, is applied in many other projects, and complies with INSPIRE.


List of Websites:
http://www.euro4m.eu