Guidelines for the computation of area-averaged monthly percentage of warm days and cold days 

Introduction
The aim of the TTNCMP of WMO CCl OPACE2 is to recommend and develop a short list of NCMPs that all countries can easily produce.  These standardised products will ease to summarise global climate reports and will not replace the ongoing detailed analysis of individual NMHSs.  There is a different range of users of NCMPs: global (e.g. GFCS, GSCU, CWS and BAMS), regional (e.g. RCC, COF) and local (e.g. agriculture, industries, research and media) (TTNCMP 2012).  

It is acknowledge that the NHMSs worldwide are providing a great deal of information on weather extremes (WMO 2007).   Besides, Tank et al (2009) guidelines help NMHSs to identify and describe changes in the weather extremes for a better service to end users.  

This report provides guidance on the computation of two NCMPs: the percentage of warm days and the percentage of cold nights and are based on the ETCCDI definitions.  The joint CCl/CLIVAR/JCOMM Expert Team (ET) on Climate Change Detection and Indices (ETCCDI) has a mandate to address the need for the objective measurement and characterization of climate variability and change by providing international coordination and helping organizing collaboration on climate change detection.  It, thus, deals with indices relevant to climate change detection by encouraging the comparison of modelled data and observations.  Issues being addressed include the practical aspects of developing guidance and materials for NMHSs – toolkits including software, documentation, and other material to guide the calculation and use of climate change detection indices and climate data homogenization, improvement of global coverage and assessment of indices (CLIVAR 2012).  The ETCCDI has defined a mix of 27 temperature and precipitation core indices for climate monitoring and trend computation that require homogenised daily temperature and precipitation data (ETCCDI 2012) .  These indices mainly calculate the annual number or percentage of days exceeding user defined threshold values and are computed using an R-based software package available online (http://cccma.seos.uvic.ca/ETCCDI/software.shtml).   

Homogenisation
It is imperative that the homogenised data are used in the computation of the bundle of the indices proposed by ETCCDI.  Homogenisation consists of detecting the shifts in climate time series that can hurdle to correctly the trends in the data series.  Consequently, the WMO guidelines on climate metadata and homogenisation stress on the importance of metadata, the different techniques in data processing and the homogenisation techniques (Aguilar et al. 2003).  The techniques have evolved over time since the first workshop in Australia 1999, however, there exists many uncertainties in the techniques to accurately detect and analyse the shifts (Vincent et al 2011).  Many studies are underway to improve upon the techniques (COSTHOME 2012) and this report will change in light of new findings.

Therefore, for the present guidelines, the computation of the percentage of warm days and the percentage of cold nights (two NCMPs) will require daily and homogenised data from selected climatological stations.  As mentioned earlier, the ETCCDI website provides the necessary tools and guidance prepare the data series and to homogenise individual station data.

However, inhomogenised data are also valuable in Climate analysis and this needs further discussions.
Area-average monthly percentage of warm days (TX90p_new) 
In fact, the ETCCDI defines TX90p as the percentage of the number of days in a year with the daily maximum temperature above the 90th percentile of the daily maximum temperatures.   For example, let TXij be the maximum temperature for a particular day i in a period j.  Furthermore, say TXin90 is the calendar day 90th percentile centred on a 5-day window for the base period 1961-1990.  Then, the percentage of time for the base period is determined where TXij > TXin90 (ETCCDI 2012).  

 The Task Team NCMP is proposing the NMHSs worldwide to be able to use the proposed software to compute the area-average monthly percentage of warm days (TX90p_new) for the upgraded base period to 1971-2000.  It requires all the homogenised climatological stations data within the jurisdiction of the particular NMHS and results in a single value for that particular month.  However, the techniques developed by the ETCCDI generate annual values.  For that, the task team is proposing the development of the R-based software.  If the period j adjusts from annual to monthly, then the software will be able to compute the monthly percentage.  The additional task is the computation of the area-average of the values from all the climatological stations.  One way to do this may be to use the Kriging method. The individual NMHS will then broadcast a single monthly value for the area-average monthly percentage of warm days.

Area-average monthly percentage of cold nights (TN10p_new)
In a similar way, the ETCCDI defines TN10p as the percentage of the number of days in a year with the daily minimum temperature below the 10th percentile of the daily minimum temperatures.   For example, let TNij be the minimum temperature for a particular day i in a period j.  Furthermore, say TNin10 is the calendar day 10th percentile centred on a 5-day window for the base period 1961-1990.  Then, the percentage of time for the base period is determined where TNij < TNin10 (ETCCDI 2012).  

Likewise, the Task Team NCMP is proposing the NMHSs worldwide to be able to use the proposed software to compute the area-average monthly percentage of cold nights (TN10p_new) for the upgraded base period to 1971-2000.  This also, requires all the homogenised climatological stations data within the jurisdiction of the particular NMHS and results in a single value for that particular month.  The R-based proposed software should be able to compute the area-average of the values from all the climatological stations.  The individual NMHS will then broadcast a single monthly value for the area-average monthly percentage of cold days.

Proposal
•	The data from the climatological stations of the individual NMHSs must be quality controlled and homogenised.
•	The software must be flexible to prompt the user to input their individual base period values and to output the area-average monthly percentage for the warms days and cold nights.
•	To include a test case - stepwise procedure from entering data to the computation of the products.    

References
COSTHOME 2012, The European Cooperation in Science and Technology Mechanism, viewed March 2012, < http://www.homogenisation.org/v_02_15/> 

CLIVAR 2012, Expert Team (ET) on Climate Change Detection and Indices, viewed on March 2012, < http://www.clivar.org/organization/etccdi/resources/indices-data>.

ETCCDI 2012, Definition of the 27 core climate change indices, viewed December 2011,     < http://cccma.seos.uvic.ca/ETCCDI/list_27_indices.shtml>

Tank AMGK, Zwiers FW& Zhang X  2009, Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, WCDMP no. 72, WMO. 

TTNCMP 2012, CCl OPACE 2 Task Team on National Climate Monitoring Products, viewed on February 2012, <http://www.metoffice.gov.uk/hadobs/opace2_tt_ncmp/>.

Vincent LA, Aguilar E, Saindou M, Hassane AF, Jumaux G, Roy D, Booneeady P, Virasami R, Randriamarolaza A, Faniriantsoa FR, Amelie V, Seeward H& Monfraix B 2011, observed trends in indices of daily and extreme temperature and precipitation for the countries of the Western Indian Ocean 1961-2008, Journal of Geophysical Research, vol. 116, D10108. 

WMO 2007, Climate information for adaptation and development needs, no. 1025.