D422Lot1.SMHI.5.1.1B: Detailed ... - climate.copernicus.eu · 2.3 Pros and cons or cost-benefit...

C3S_422_Lot1_SMHI – D5.1.1B |

Copernicus Climate Change Service

D422Lot1.SMHI.5.1.1B:

Detailed workflows of each case-study

on how to use the CDS for CII

production and climate adaptation

Full Technical Report:

Tailored climate information in pilot

basins for food security and agriculture

in the Andes

Juan Jose Nieto

International center for research on El Niño - CIIFEN

REF.: C3S_422_Lot1_SMHI D5.1.1B

C3S_422_Lot1_SMHI – D5.1.1B |


Summary

The document describes the provision of tailored climate forecasts to users in the

Andean region of Colombia and Ecuador taking advantage of the high quality model data provided under the Climate Data Store (CDS) and the Copernicus

Climate Change Service (C3S). Both basins are dedicated to agriculture. The pilot area in Colombia is mostly dedicated to coffee bean crops and the pilot area in Ecuador is mostly dedicated to maize crops. The obtained results are very

important in terms of information for decision making in both, climate variability and climate change. Stakeholders across the region expressed how important it

is for them to have clear, reliable, tailored and comprehensive information to make better decisions.

Rainfall data from ECMWF seasonal forecast SEAS5 were aggregated monthly. A

global high-resolution precipitation dataset was then used to downscale SEAS5 hindcast data to a 0.05 degree grid resolution using the statistical analysis tool

CPT (Climate Predictability Tool). Downscaled outputs were tailored to show results in other ways than traditional terciles category maps. We tailored maps to show probabilities of exceedance of upper and lower percentiles, probability of

exceedance of certain critical threshold, probability of having similar conditions as in El Niño or La Niña years, among others.

In addition, climate change assessments were also made using Climate Impact indicators (CII) and station data. We used CII calculated within C3S_422_Lot1_SMHI, such as annual and monthly maximum and minimum

values of daily maximum and minimum temperature (also known as TXX and TNN), for the RCP4.5 scenarios and the 2011-2040 period. The same CII were

calculated using station data from the National Meteorological and Hydrological Services (NMHS), for the past period 1993-2015. An example finding of the assessment is that the region may expect a clear rising of air temperature which

affects crops in both basins.

With these results, stakeholders are aware of recent changes in climate patterns

in each region and how climate variability and its related extreme events are causing more impacts than in the past, also by the construction of vulnerability

related to these climate events. Authorities are aware that vulnerability has to be reduced through adaptation methods co-produced with national institutions, and agree that a region adapted to climate variability is more resilient to climate

change.

The added value of the CDS and its toolbox, and the C3S data, was the easy

access to global seasonal forecasts, climate change, and CII data. Global CII increase the understanding of climate change impacts because they provide more targeted and tailored information than the climate variables themselves.

Moreover, a CII can have more predictability than the original variable and can be used for seasonal forecasting.


C3S_422_Lot1_SMHI – D5.1.1B |

Contents 1- Case study description ........................................................................................................ 4

1.1 Issue to be addressed ......................................................................................................... 4

1.2 Decision support to client .................................................................................................. 4

1.3 Temporal and spatial scale ............................................................................................... 4

1.4 Knowledge brokering ........................................................................................................... 5

2- Potential adaptation measures ........................................................................................ 5

2.1 Lessons learnt ........................................................................................................................ 5

2.2 Importance and relevance of adaptation .................................................................... 5

3- Contact ...................................................................................................................................... 7

3.1 Purveyors ................................................................................................................................. 7

3.2 Clients/users ........................................................................................................................... 7

4- Data production and results ............................................................................................. 7

4.1 Step 1: S5 data download ................................................................................................. 8

4.2 Step 2: Quality control, Bias calculations and downscaling of seasonal

forecasts ........................................................................................................................................... 8

4.3 Step 3: CII calculation ...................................................................................................... 10

4.4 Step 4: Presentation of downscaled outputs ........................................................... 14

4.5 Step 5: Web mapping ....................................................................................................... 16

4.6 Step 6: Feedback from NMHSs and users................................................................. 18

5- Conclusion of full technical report ................................................................................ 19


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1- Case study description

Local authorities in Colombia and Ecuador need rainfall forecasts, probabilities of

extreme temperatures, and probability of exceeding certain thresholds (more than 90 mm and less than 30 mm of rainfall) to successfully manage local agriculture processes in the future. This kind of information is expected to reduce

crop losses, and improve farming practices (crop cycles) by providing authorities and farmers with useful and tailored climate change information.

1.1 Issue to be addressed

In case of expected above or below normal weather conditions, the authorities may implement plans to ensure that farmers can deliver their harvest to

distribution centers. Farmers need forecasts to plan crop cycles. Currently, local authorities and farmers do not have forecast information at the required spatial

resolution, in the appropriate language, nor in a format easy enough to understand. Forecast use is limited by these different factors. This situation

occurs because forecasts are made for large regions (at national or regional level) and only in terms of probability in tercile categories. Another issue is the limited capacity of the meteorological services to provide information at local

level.

1.2 Decision support to client

Climatic conditions above normal means activating plans to guarantee crops transportation from the production sites to the distribution centers, since excessive rainfall makes access to the fields difficult. For farmers it means

rotating crops or modifying the planting plan.

For expected below normal conditions, authorities may consider providing

irrigation mechanisms or alternative sources of products. For farmers it may mean rotating crops or modifying the planting plan. Users are aware about inherent uncertainty of the probabilistic forecast. They were advised during

training workshops about how to deal with the probabilistic forecast taking into account the whole spectrum of chances, not only the more likely scenario: this is

of special importance to construct trust between providers and users of climate information.

All actions are focused on two of the pillars of food security:

Access to agricultural products (Availability) Offer of agricultural products (Stability)

1.3 Temporal and spatial scale

The case study is focused on a seasonal time scale (monthly and quarterly). Decision-making is implemented at the basin scale of a hundred square

kilometers (Chinchina basin (Colombia), Jubones river basin (Ecuador)).


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1.4 Knowledge brokering

The clients in the two pilot basins have dealt with the knowledge purveyor previously, and there is a clear understanding of the clients' needs. Contacts with

the clients are conducted through the National Meteorological and Hydrological Services (NMHSs), and there are specific contact people for each pilot basin.

After an assessment of each basin, the knowledge purveyor has found that both basins need similar climate variables. However, different types of crops, technical development and level of service received by NMHSs can be found in the two

basins.

Client needs are surveyed through regular in-person meetings. Clients, both

authorities and farmers, need better spatial resolution in climate. Tailoring forecasts in ways different to tercile categories will help better communicate and provide information for better decision making. Communication of results to the

clients was done through simple web interface.

2- Potential adaptation measures

2.1 Lessons learnt

A proxy of climate change adaptation measures can be adapted to the seasonal scale, as a community prepared for climate variability is more resilient to climate

change. The lessons learnt are that although the projections or seasonal predictions are not infallible, they provide an idea of what can happen in terms of climate in the long and short term. As authorities are constantly removed,

sustainable strategies for proposed measures must be defined based on national institutions.

It is necessary that the information is treated in an inter-sectoral space in order to be able to analyze the possible impacts in different sectors and to have more integrated proposals, which are more likely to be successful. Further, the

information needs to reach the relevant management level. To do this, considering the level of education and internet access, a multimodal approach

should be used to disseminate information: local radios, extensionists and government programs working with the same products, producer associations, religious services, commercial houses, etc. Climate change information and

possible impacts have to be translated into a simple language for easy understanding by those who will potentially be affected. The definition of possible

ways to face and reduce impacts should consider the knowledge from the local practitioners as they are those who have already experienced similar situations and have traditional practices to cope with similar events.

2.2 Importance and relevance of adaptation


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Usually, climate and variability assessments are based on global projections and

poorly quality-controlled data from meteorological stations, and generally do not consider food security concepts. The focus is usually on specific crops, without

considering the vulnerability of that crop to climate change, or as a component of the food sector as a whole. Traditionally these assessments are crop-centric and not user-centric.

Climate information can be particularly helpful to anticipate, prepare for and respond to agriculture or food security risks, to address problems triggered by

climate extremes (i.e., droughts, thermal extremes) as well as longer term risks associated with climate change (e.g., increased frequency of floods, desertification).

The service is driven by the needs of the user community, products are tailored for local use, and resources are based on collaboration and consultation with

local partners and stakeholders (authorities and farmer associations). Extension services in agriculture are often the link between the scientific community and the agricultural users.

2.3 Pros and cons or cost-benefit analysis of climate adaptation

Climate change will act as a hunger risk multiplier, exacerbating the risk factors that impact food security. Without significant efforts to improve livelihoods and

build resilience, climate change is projected by 2050 to increase the number of people at risk of hunger by 10 to 20 per cent and to increase the number of malnourished children by 21 per cent. Some benefits arising from decision

making based on this approach of adaptation can be:

Identification of extreme climate hazards over agriculture and food

security; Identification of populations vulnerable to climate hazards; Climate sensitive decisions in the agriculture and food security value

chains;

2.4 Policy aspects

Results can be useful to improve policies on plant and animal pest and disease control strategies; pesticide and herbicide applications, fertilizer management, farm and irrigation management; decisions on export and import of agricultural

inputs and products and marketing of agriculture and food security products.


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3- Contact

3.1 Purveyors

Rodney Martinez, Juan José Nieto, Felipe Costa, Pier Maquilón and Raúl Acevedo (CIIFEN), Edificio Fundación El Universo, Guayaquil, Ecuador

3.2 Clients/users

Local communities in Colombia and Ecuador

4- Data production and results

Data production and results start with ECMWF SEAS5 data available in the CDS catalogue for the regions of interest for the case study (Colombia and Ecuador).

Data corresponds to precipitation and mean, maximum, minimum temperatures for the period 1993-2015. Bias for some grid points of the seasonal outputs and CHIRPS dataset, was calculated through correlations and root mean square error

with meteorological stations data from the National Meteorological and Hydrological Services (NMHS) of Colombia (IDEAM) and Ecuador (INAMHI).

Stations data were quality controlled using the statistical package RClimdex, which also calculates climate indices such as consecutive dry days, consecutive wet days, yearly total precipitation amount, 95th precipitation percentile, heat

waves or warm days and cold waves or cold nights, and the trend of these indices over the analyzed period.

Monthly-aggregated rainfall from SEAS5 data and a global high-resolution precipitation dataset were used to downscale SEAS5 hindcast data to a 0.05 degree grid resolution using the statistical analysis tool CPT. Downscaled outputs

were tailored to show results in other ways than traditional terciles category maps. We tailored maps to show, among others, the probabilities of exceeding

the 80% percentile and not reaching the 20% percentile respectively, the probability of exceeding critical thresholds (more than 90 mm and less than 30 mm in this case study), and the probability of having similar conditions as in El

Niño or La Niña years.

Maps were uploaded in the web to allow users to explore results in more detail in

order to support their decisions based on more comprehensive information. Finally, a communication strategy was co-designed with the NMHS to enhance

http://ciifen.org/index.php


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communication in both ways, from providers (NMHS) to users (Authorities and

farmers), and feedback from users to providers.

4.1 Step 1: S5 data download

Description:

ECMWF SEAS5 seasonal forecasts (S5, hindcast period 1993-2015) of

precipitation and mean, maximum, minimum temperature were provided by the C3S_422_Lot1_SMHI contract for all initializations (7 months ahead). S5 is

retrievable from the CDS catalogue. Daily data from all members were used to create monthly aggregated and ensemble means of both, precipitation and temperature.

Results:

Datasets of precipitation and temperature for each month were created with data

from 1993 to 2015.

4.2 Step 2: Quality control, Bias calculations and downscaling of

seasonal forecasts

Description:

Quality control of station data

Quality control analysis was applied to stations data. Data were checked for

outliers, duplicates, and inconsistency. The RClimDex QC for station data, performs the following procedure: 1) Replace all missing values (currently coded as -99.9) into an internal format that R recognizes (i.e. NA, not available), and

2) Replace all unreasonable values into NA. Those values include a) daily precipitation amounts less than zero and b) daily maximum temperature less

than daily minimum temperature. In addition, QC also identifies outliers in daily maximum and minimum temperature. The outliers are daily values outside a region defined by the user. Currently, this region is defined as the mean plus or

minus n times standard deviation of the value for the day, that is, [mean – n*std, mean+n*std]. Here std represents the standard deviation for the day and

n is an input from the user and mean is computed from the climatology of the day. All data was quality controlled before calculation of climate indices.


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Evaluation of the datasets

We evaluated how well model outputs and gridded datasets fit observations, based on the calculation of the bias, the correlation and the root mean square

error. Bias calculations were made based on S5 data outputs and on the CHIRPS dataset. The bias in S5 and CHIRPS data was calculated to assess the quality of the datasets (measuring the difference between the simulated value and the

measured value of the variable (observations from station data)).

The correlation indicates the strength and direction of a linear relationship and

proportionality between two statistical variables; it is considered that two quantitative variables are correlated when the values of one of them vary

systematically with respect to the homonymous values of the other)

RMSE (Root Mean Square Error), is a measure of quantitative performance, and

consists of the square root of the sum of the squared errors. The RMSE amplifies and penalizes with greater force those errors of greater magnitude

Seasonal forecast model

Data from S5 and the CHIRPS global gridded dataset were used to create a

model for the season October-December. Data from S5 model outputs were set as the independent (explanatory) variable and data from CHRIPS were set as dependent (response) variable for precipitation.

Downscaling of seasonal forecasts

S5 monthly rainfall outputs were further downscaled to 0.05 degrees gridded

data for all pilot basins. Downscaling was performed using the IRI´s Climate Predictability Tool (S5 rainfall as predictor, CHIRPS data as response variable) and tailored for sectoral applications. Monthly-aggregated hindcast data were

converted into Climate Predictability Tool (CPT) input file format. CPT is a software package developed by the International Research Institute of Columbia

University for constructing a seasonal climate forecast model, performing model validation, and producing forecasts given updated data. Its design has been tailored for producing seasonal climate forecasts using model output statistic

(MOS). Although the software is specifically tailored for these applications, it can be used in more general settings to perform canonical correlation analysis (CCA),

principal components regression (PCR), or multiple linear regression (MLR) on any data, and for any application.

Results:


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Model outputs and gridded datasets were evaluated and then downscaled through CPT. Adjustment of the bias was not carried out because the statistical method used for downscaling transforms data into categorical outputs (terciles

for above, and below normal probabilities).

4.3 Step 3: CII calculation

Description:

Climate indices (for the period 1993-2015 as reference climatology) were calculated using station data. The CII were calculated using the software package RClimdex. This software was designed to compute all 27 core indices

recommended by the CCl/CLIVAR Expert Team for Climate Change Detection Monitoring and Indices (ETCCDI) as well as some other temperature and precipitation indices with user defined thresholds. The package performs a user-

guided quality control of precipitation, maximum temperature and minimum temperature data as it is described in Step 2. Data Quality Control is a

prerequisite for indices calculation. Daily precipitation, maximum temperature and minimum temperature data from 20 stations of the meteorological services of Colombia (IDEAM), and Ecuador (INAMHI) were used. Although all 27 core

indices were calculated, here we focused on; warm days (TXX) and cold nights (TNN) over a historical period. The respective indicators from

C3S_422_Lot1_SMHI for the period 2011-2040 for RCP4.5 were also used in this analysis.

Results:

A trend was calculated with the yearly indices of the stations data. Trends allow estimating changes of extremes for any particular location during the past

decades. Statistical significance was also calculated for each index.

Figure 1 shows the trend in maximum temperatures for the period 1993-2015. Arrows represent the location of the stations. Blue arrows pointing up represent

a positive (increasing) trend and the red arrow represents a negative (decreasing) trend. Most of the stations show an increase in maximum

temperature in the past years. Figure 2 shows the trend in minimum temperatures from 1993-2015. All the stations show an increase in minimum

temperature during the last years. No negative trend was found.


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Figure 1. Trend in maximum temperatures from 1993 to 2015 in Chinchina basin (Colombia). Blue arrows represent positive trends and the red arrow represents a negative trend.

Figure 2. Trends in minimum temperature from 1993 to 2015 in the Chinchina basin (Colombia). Blue arrows pointing up represent positive (increasing) trends.


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We then used CII calculated within C3S_422_Lot1_SMHI to assess how

maximum and minimum temperature monthly climatology may change in both basins, compared with present tendencies. Figures 3a and 3b show the change of

maximum temperature during the year for the period 2011-2040 for (a) the Jubones-Ecuador basin, and (b) the Chinchina-Colombia basin. Models predict a uniform monthly increase in maximum temperature around 2.0°C. Figures 3c

and 3d show the change of minimum temperature during the year for the period 2011-2040 for (c) the Jubones-Ecuador basin, and (d) the Chinchina-Colombia

basin. Models predict a uniform monthly increase in maximum temperature average, in about 1.5° to 2.0°C in Chichiná and Jubones, respectively.


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Figure 3. Change of maximum temperature (a and b) and minimum temperature (c and d)

climatology for the period 2011-2040 compared with present temperature in the Jubones and Chinchina basins.

According to the models ensemble means of the RCP4.5 scenario, monthly

maximum and minimum values of daily maximum and minimum temperature will increase by 1.5 to 2°C in both basins for the period 2011-2040. Significant

positive trends in the past years were thus further confirmed by climate projections, which predict an increase in maximum and minimum temperature in both basins.

Indicators were used to estimate possible climate impacts to define some preliminary adaptation measures. However, discussions with users focused on

the work done at the seasonal scale more than on the work done on climate change impacts.


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4.4 Step 4: Presentation of downscaled outputs

Description:

Downscaled outputs are presented in terms of probability of exceedance of upper and lower percentiles, probability of exceedance of certain critical threshold, probability of having similar conditions as in El Niño or La Niña years, among

others.

Results:

Maps were created using the R programing language. Units are in millimeters and probability percentage for the categorical outputs. Figures 4 and 5, respectively show examples of outputs for rainfall amount and rainfall probability

for each terciles category.

Figure 4. Statistical downscaled forecast of monthly precipitation amount in mm, for central

Colombia using CPT with S5 data and CHIRPS data. Resolution 6 Km.


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Figure 5. Statistical downscaled forecast of monthly precipitation tercile probabilities for central

Colombia using CPT with S5 data and CHIRPS data. Resolution 6 Km.


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4.5 Step 5: Web mapping

Description:

Tailored downscaled outputs and vulnerability maps were uploaded into a web

system. The system was developed using THREDDS data server technology in order to enhance the interaction of users with the demonstration products. This

allows users to picture climate threats and the vulnerability of the basins, in order to facilitate decision making based on high-quality and high-resolution data. Figures 6 and 7 show the web systems where maps are uploaded using

web mapping tools to let users overlap different layers. Users can define transparency of each layer to see where a high threat matches a high vulnerable

area.

Figure 6: Map showing vulnerability of Jubones basin (Ecuador) to heavy rains, overlapped with a

forecast of rain for November 2015 (mm). Users can use transparency of each layer to picture

information and determine “hot spots”.


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Figure 7: Map showing vulnerability of Chinchiná basin (Colombia) to floods, overlapped with a

forecast of rain for November 2015 (mm). Users can use transparency of each layer to picture

information and determine “hot spots”.

A web story map was developed under the C3S_422_Lot1_SMHI. The figures below show how the web story map will tell users about the benefits of the

approach of tailored information for decision making to support food security in the Andes. The web presence is showing how information can be sector-tailored

for better and informed decisions.


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Results:

4.6 Step 6: Feedback from NMHSs and users

Description:

Meeting with NMHS technicians were organized to discuss the concept of the

study case. A communication (provider-user-provider) strategy with local


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authorities was also developed. The strategy will consider both ways of

communication from providers to users, and feedback from users to providers. Online workshops with local authorities were organized to explain how to

interpret and use the information provided, including limitations.

During July 2018, workshops with users, especially from the agriculture sector in Colombia (coffee bean farmers grouped in CENICAFE association), showed the

importance of having such a system for agriculture practices. Colombian farmers insisted on the need of having more support from the national institutions that

provide information in order for them to be better informed and to take better decisions based on such information. They understood the limitations of any forecast in terms of uncertainty, but also understood that having tailored forecast

can determine the difference between loss and gain.

5- Conclusion of full technical report

Communities are aware of recent changes in climate pattern in each region and how climate variability and its related extreme events are causing more impacts than in the past. They understand that the increase of climate impacts is caused

by a combination of more extreme events frequency, but also the construction of vulnerability related to these climate events. They are aware that vulnerability has to be reduced through adaptation methods, co-produced with national

institutions and local authorities.

In this context, a relevant added value of the C3S data is the possibility to access

reliable and scientifically robust information that allows co-designing adaptation measures among users and technicians with more confidence. Based on the assessment of likely change in future climate patterns in the region, adaptation

measures were defined. Some of them are:

Provision and strengthening of irrigation systems

Promotion of agro-family orchards

Management of organic fertilizers for soil moisture improvement

Promotion of trees and forage planting

Promotion of crops rotation

Tailored climate information, as proposed in this case study, allows users to take

better informed decisions because it is more comprehensive since it is usually referenced to a well-known event in the past, for example the last rainy season,

ENSO-like rainfall, and so on.

The case study demonstrates that SEAS5 global seasonal forecasts provided by ECMWF can be easily downscaled, with low-cost methods, into basin scale. Global

climate impact indicators CII increase the comprehension of what climate change can cause because they bring more information than the simple change in a


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climate variable itself. A CII can have more predictability than the original

variable.

D422Lot1.SMHI.5.1.1B: Detailed ... - climate.copernicus.eu · 2.3 Pros and cons or cost-benefit...

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