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History of Modifications

Acronyms

Scope of the document

This document serves as Product user Guide Specifications for Agroclimatic Indicators datasets, as part of the C3S Global Agriculture Sectoral Information Systems (SIS). More information about the project can be found at https://climate.copernicus.eu/global-agriculture-project.

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2. Executive summary 2. Executive summary

Executive summary

The C3S Global Agriculture (SIS) project aims to develop climate services in support of decision- making in agriculture sector. It does so in a process of co-creation with partners representing international crop research, international agricultural policy development and commercial agricultural consultancy services.

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A total of 26 indicators are provided, covering the global land area at the spatial resolution of 0.5°x0.5° lat-lon grid. For many indicators a dekad¹resolution is used, a unit often used in agricultural sciences that allows aggregation of (summable) indicators to varying growing seasons and crop phenological phases around the world, more precisely than a monthly resolution would. A brief review of the agroclimatic indicators provided by C3S global agrilculture SIS is given in tables below:

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From this list the following indicators: CDD, CFD, CSDI , WSDI, CSU, CWD have a seasonal resolution, and the GSL has an annual resolution. All others have dekadal resolution.

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3. Product description 3. Product description

Product description

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3.1. Introduction 3.1. Introduction

Introduction

Agroclimatic indicators represent features of the climate that are used to characterise plant-climate interactions. They can be derived from daily or monthly meteorological variables (e.g. temperature and rainfall). They are often used in species distribution modelling and related ecological modelling techniques, and also in studying phenological developments of plants under varying climate conditions.

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All of the generic agroclimatic indicators are pre-computed and the crop-specific indicators are computed on-demand, using standard CDS Toolbox workflows. The crop specific indicators cover four main crops of global interest: wheat, maize, rice and soybean.

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3.2. Geophysical product description 3.2. Geophysical product description

Geophysical product description

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3.2.1. Generic agroclimatic indicators 3.2.1. Generic agroclimatic indicators

Generic agroclimatic indicators

C3S Global Agriculture SIS agroclimatic indicator products include 26 indicators and cover global land areas. All indicators are computed from realizations of daily data, derived from two essential climate variables (ECV):

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A total of 26 indicators were adapted from the European Climate Assessment & Dataset project (ECA&D; Klein Tank, 2007) collection for their general relevance to agriculture, especially the priority crops, but not specific to any particular crop. Table 1 lists the agroclimatic indicators provided by C3S global agriculture SIS along with information on their application in agriscience.

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Note: The winter season (DJF) of a year is composed of December the current calendar year and January and February of the following calendar year (e.g. DJF 2000 is composed of Dec 2000, Jan 2001 and Feb 2001)

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3.2.2. Input Data 3.2.2. Input Data

Input Data

To generate the agroclimatic indicators for historical and future time periods, bias-corrected climate datasets provided through Inter-Sectoral Impact Model Intercomparison Project (ISIMIP²) have been used. ISIMIP is a community-driven climate impacts modelling initiative aimed at contributing to a quantitative and cross-sectoral synthesis of the differential impacts of climate change. ISIMIP project was organised into 3 main simulation rounds, of which the 'ISIMIP Fast Track' product has been used to produce the present indicator set.

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The ISIMIP Fast Track climate forcing data is used for generating the current Agroclimatic indicators. Climate variables from ISIMIP Fast Track are bias-corrected using method described by Hempel et al. (2013). Table 2 shows the availability of ISIMIP Fast Track datasets for different GCM/emission scenarios, covering 1951 - 2099.

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For the historical 'observations', the "Watch Forcing Data methodology applied to ERA-Interim (WFDEI)" (Weedon et al. 2014) were used to generate observational historical Agroclimatic indicators for the 1981-2010 climatological period. This datasets is available at the same spatial resolution of ISIMIP climate datasets and covers the time range of 1979 to 2013 (Weedon et al. 2014).

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3.2.3. Crop specific parameters and maps 3.2.3. Crop specific parameters and maps

Crop specific parameters and maps

To facilitate the assessment of tailored crop-specific indicators we have chosen not to precalculate these as the number of options is near indefinite. Instead we provide the necessary information to allow the user to calculate these on demand, such as sowing date, harvest date, growing range of min and max temperatures, thermal requirements, geographical distribution, etc. This way the user can generate outputs specific to the crops of interest.

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*) only provided for wheat and maize; definition of each mega environments in terms of climatic constraints differ per crop; see ATBD document.

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3.3. Product target requirements 3.3. Product target requirements

Product target requirements

Data from over 30 Global Climate and Earth System Models (GCMs and ESMs) contributing to the Climate Model Inter-comparison Project phase 5 (CMIP5) are available for use in climate impacts assessments. The CMIP5 provided a framework for coordinated climate change experiments aimed at:

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That, and the fact that AgERA5 was not yet available at the production date, is the reason that for historic data WFD have been used.

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3.4. Product Gap analysis 3.4. Product Gap analysis

Product Gap analysis

For many users in the agricultural community also assessments of crop development and associated climate anomalies for the running cropping season are highly relevant. This is especially true for the agro-policy and the agro-business communities, as early indications of production anomalies are of paramount importance for tax/subsidies and price volatility.

For that reason the agro-climatic indicators should ideally also be available in near real time, and thus be precalculated for the historical AgERA5, and then to e daily updated once ERA5T becomes online in the CDS.

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4. Data usage information 4. Data usage information

Data usage information

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4.1. Practical usage considerations use 4.1. Practical usage considerations use

Practical usage considerations use of products

Despite the recent progress in the development of GCMs, they still exhibit a number of significant systematic biases in their ability to simulate key features of the observed climate system (Randall et al. 2007). Although the application of the bias correction has shown that it effectively improves both the mean and the variance of the precipitation and temperature fields (Hagemann et al.
2011), the users should still be cautious regarding the use of data from a single model in their applications.

Bearing in mind the consideration that has been given in selecting the GCMs, it is recommended to use a multi-model ensemble of agroclimatic indicators instead of individual models in order to achieve reliable results in any impact assessment applications. Also temporal aggregation of data over climatological periods are recommended to remove the uncertainties associated with inter- annual variation of GCM results.

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4.2. Known Limitations of product 4.2. Known Limitations of product

Known Limitations of product

While the accuracy of GCMs in simulating the large-scale atmospheric circulation has improved markedly in recent years, global models have difficulty resolving the processes that govern local precipitation. One of the drawbacks of the bias correction method that has been used in ISIMIP Fast

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Despite the criteria that are imposed to avoid generating unrealistic precipitation values, comparison between bias corrected data and observational data indicated high values in some regions. Hence the results should be interpreted with caution.

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5. Data access information 5. Data access information

Data access information

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5.1. Climate Data Store 5.1. Climate Data Store

Climate Data Store

The Copernicus Climate Change Service provides data storage infrastructure and make ECV data products available through the CDS. The store provides not only consistent estimates of ECVs, but also climate indicators, and other relevant information about the past, present, and future evolution of the coupled climate system, on global, continental, and regional scales. It supports users with data dissemination and visualization tools.

The C3S 422 Lot1 service provides dedicated level 2 user support to the CUS Jira Ticketing Service. In addition to submitting enquires through the portal a knowledge base is available to users which can be searched for information.

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5.2. C3S Global Agriculture data 5.2. C3S Global Agriculture data

C3S Global Agriculture data

All C3S422 Global Agriculture data, data stream 1 agroclimatic indicators, are expected to be available via the CDS starting in Q2 2019.

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5.3. User Support 5.3. User Support

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User Support

A dedicated service desk has been set up, the Copernicus User Support (CUS) team, which provides support to users of the CAMS and C3S services at ECMWF. All enquiries about the C3S422 Global Agriculture datasets must be submitted through the service desk where appropriate agents will deal with it.
There is a portal (http://copernicus-support.ecmwf.int) where customers can submit enquiries using a form (split into "Data Request", "Documentation and Scientific Questions", "Events, Media and Legal" and "Report an Incident"). The information provided in this form is received by the CUS.

Once submitted, the user may add comments or further information to the issue, including responding to questions / requests for additional information from the support team.

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6. Concluding remarks 6. Concluding remarks

Concluding remarks

By providing access to these generic and crop specific agro-climatic indicators C3S will greatly facilitate the use of climate data by the agricultural community. User workshops repeatedly confirmed interest in these data. The route chosen here, providing pre-calculated indicators based on a selection of bias corrected GCMs/RCPs scenarios, will a) make life much easier for agronomists with relatively limited expertise on climate change, and b) immensely improve response time of any online apps that users may built to interact with such data.

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• Pre-calculated these indicators for the AgEra5 archive and especially, also for the near real time data from this source once these become available; many users have stressed repeatedly the great potential value of analysing the running cropping campaign.
• To allow the more experienced agricultural users complete freedom in the choice of climate input for indicator calculation it is recommended to make available the (python) software used for offline calculation of the indicators under this contract, as an online tool in the C3S Toolbox.

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9. Appendix 9. Appendix

Appendix

[The information regarding the bias correction methodology for ISIMIP Fast Track is directly provided from the fast track bias correction fact sheet, available at https://www.isimip.org/documents/16/Fact_Sheet_Bias_Correction.pdf]

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We modified the Piani et al. (2010) approach to preserve the absolute temperature changes and the relative changes in precipitation and other variables as fundamental elements of the GCM projections. Here we describe the algorithm.

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9.1. Adjustment of the monthly mean valu 9.1. Adjustment of the monthly mean valu

Adjustment of the monthly mean values

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9.1.1. Temperature 9.1.1. Temperature

Temperature

The bias correction algorithm for temperature preserves the monthly mean values provided by the GCM, by adding a grid-point and month specific (one for January, one for February etc.) constant offset. In this way the absolute changes in temperature are not modified by the bias correction but the reference starting level is adjusted to the observational level provided by a 40-year average of the Watch data.

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$$Tmin(max)_{BC}=mean[Tmin(max)_{GCM}-T_{GCM} + T_{GCM}$$

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9.1.2. Precipitation 9.1.2. Precipitation

Precipitation

For precipitation data we use a multiplicative correction to adjust the monthly mean values in the historical period to the observed climatological monthly mean values. This ensures that the monthly mean precipitation values are preserved up to a constant multiplicative factor. The monthly means are multiplied by a grid-point and month specific (one for January, one for February
etc.) constant correction factor (hereafter μ). We thereby ensure that the relative change in precipitation as described by the original GCM data is preserved.

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Snowfall is not directly bias corrected, but rather the ratio of snowfall to rainfall in the original GCM data is preserved, based on the bias-corrected rainfall data.

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9.1.3. Other variables 9.1.3. Other variables

Other variables

Monthly values of the other variables that are also subject to positivity constraints are corrected in a multiplicative way as described above for precipitation. The only exception is wind. In the case of wind, the magnitude of wind is corrected using the multiplicative algorithm. The individual wind components are then derived by preserving the direction of the original GCM data.

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9.2. Adjustment of the daily variability 9.2. Adjustment of the daily variability

Adjustment of the daily variability

As described above, we adjust neither the monthly variability of the temperature information in absolute terms, nor the monthly variability of the other variables in relative terms. However, we do adjust the daily variability around the monthly mean values as described below. The method is similar to the correction of the daily variability in Haerter et al. (2011).

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9.2.1. Temperature 9.2.1. Temperature

Temperature

The daily variability of the temperature data is simply adjusted to reproduce the variability of the observed data. The data is processed as follows:

1. Subtract the monthly means from both data sets.
2. Multiply the residual daily variations by a constant month and grid point specific factor, thereby matching the variance of the simulations to the variance of the observations.
3. These bias corrected daily variations are afterwards added to the bias corrected monthly means provided by the GCM.

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9.2.2. Precipitation 9.2.2. Precipitation

Precipitation

For precipitation we again adopt a multiplicative approach, which adjusts the relative variability. The data is processed as follows:

1. Normalize the daily precipitation data from the GCM and the Watch data set by dividing by their monthly mean values. The daily variability of dry months, specified by a certain threshold, is not modified.
2. After normalization of the wet months, map the distribution of the simulated data to the distribution of the observed daily data using a transfer function [as introduced by Piani et al. (2010) and applied to the non-normalized data within Water-MIP]. The transfer function corrects both the frequency of dry days as well as the distribution of the precipitation intensity to the observed statistics.
3. For the future projections, apply the generated transfer functions to the normalized daily precipitation of wet months.
4. Multiply the transferred data by the bias corrected monthly mean values. By ensuring the mean value of the transferred normalized daily data is equal to one (by simply dividing by the associated mean value) we ensure that the corrected monthly mean values are preserved when factoring in the daily variability.

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9.2.3. Other variables 9.2.3. Other variables

Other variables

For the other variables we also use the same multiplicative approach as for precipitation. However, in these cases the situation is simplified as it usually does not require a treatment of months or days with mean values of zero.

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7. References 7. References

References

Haerter, J., Hagemann, S., Moseley, C. and Piani, C., 2011. Climate model bias correction and the role of timescales. Hydrology and Earth System Sciences, 15, pp.1065-1073.

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Weedon, G.P., Balsamo, G., Bellouin, N., Gomes, S., Best, M.J. and Viterbo, P., 2014. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA‐Interim reanalysis data. Water Resources Research, 50(9), pp.7505-7514.

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