Product Users | Explore the simulations

Here, you can
  1. check the GLOSSARY which contains a list of abbreviations and climate variable names including a short description
  2. FILTER or refine your selection from a list of various settings, e.g. selecting a specific GCM, RCM, ...
  3. explore available CLIMATE MODEL SIMULATIONS based on your selection and get a quick link to the data (redirected from http://climexpl.knmi.nl)

TIPS | Climate projections describe a range of possible climate outcomes.

Read More Climate projections are based on different climate models with different set-ups. How should I use this information? It is important to evaluate the climate models’ ability to simulate changes, and one way to do so is to examine how they reproduce the mean seasonal cycle in temperature and precipitation. We examine model output collected from the Climate Model Intercomparison Project - Phase5 (CMIP5), the Coordinated Regional Climate Downscaling Experiment over Europe (EURO-CORDEX), and the Empirical-Statistical Downscaling project (ESD) at the Norwegian Meteorological Institute to provide the best estimates of global/regional/local climate signal that in turn can be used in impact studies. Click on the dashboard to navigate between other items.

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Product Users | Models' biases

Here, you can
  1. select a region to navigate through various predefined regions (EURO-CORDEX, PRUDENCE, European countries)
  2. modify the default settings and select the output type (e.g. chart, boxplot) and values (e.g. bias, change)
  3. evalutate biases in monthly mean air Temperature statistics
  4. evaluate biases in monthly precipitaiton totals statistics

TIPS

TIPS | One way to assess the skill of climate models is to examine the model biases in reproducing the seasonal cycle. Here, you can navigate between (CMIP5, A) global and (EURO-CORDEX, B) regional climate models, modify the settings so that they fit your needs, and explore how the models reproduce the monthly mean air temperature and precipitation totals over a number of pre-defined regions. You can additionally click on the dashboard menu to navigate between other evaluations of climate model simulations.

1. Select a region : Explore and navigate through various regions (AR5 predefined regions)

The regions used the AR5 Reference Regions which include 26 regions defined in SREX. In addition to these regions, the Arctic, Antarctic, South Asia and South-East Asia) and three global analysis domains: land only, sea only and all points. Read more
Demo video
You can navigate between the various predefined regions.

2. Settings & Outputs : Modify the default settings and select the output type and values.


You can navigate between an intermediate (RCP4.5) and a high concentration (RCP8.5) pathways.

You can navigate between one control and two (near and far) future time horizons.

You can modify the layout of the chart to display individual simulations or the envelope-based on all simulations.

You can display values for various statistics such as the mean and the spatial standard deviation. The spatial correlation are only computed between historical simulations and the reference data for the present (1981-2010).

You can filter the simulations and keep only identical simulations for all climate variables such as precipitation and temperature.

3. Bias in monthly mean air Temperature

4. Bias in monthly precipitation totals

1. Select a region : Explore and navigate through various regions (EURO-CORDEX, PRUDENCE, European countries)

You can navigate between the various predefined regions such as Europe (EURO-CORDEX domain, PRUDENCE regions, and national regions

2. Settings & Outputs : Modify the default settings and select the output type and values.


You can navigate between an intermediate (RCP4.5) and a high concentration (RCP8.5) pathways.

You can navigate between one control and two (near and far) future time horizons.

You can modify the layout of the chart to display individual simulations or the envelope-based on all simulations.

You can display values for various statistics such as the mean and the spatial standard deviation. The spatial correlations are computed only between historical simulations and the reference data for the present (1981-2010).

You can filter the simulations and keep only identical simulations for all climate variables such as precipitation and temperature.

3. Bias in monthly mean air Temperature

4. Bias in monthly precipitation totals



Product Users | Seasonal Cycle

Here, you can
  1. select a region to navigate through various predefined regions (EURO-CORDEX, PRUDENCE, European countries)
  2. modify the default settings and select the output type (e.g. chart, boxplot) and values (e.g. bias, change)
  3. evalutate the seasonal cycle of monthly mean air Temperature statistics
  4. evaluate the seasonal cycle of monthly precipitaiton totals statistics

TIPS | One way to assess the skill of climate models is to examine how they reproduce the seasonal cycle. Here, you can navigate between (CMIP5, A) global and (Euro-CORDEX, B) regional climate models, modify the settings so that they fit your needs, and explore how the models reproduce the monthly mean air temperature and precipitation totals over a number of pre-defined regions. Click on the dashboard to navigate between other evaluation items.

1. Select a region : Explore and navigate through various regions (AR5 predefined regions)

The regions used the AR5 Reference Regions which include 26 regions defined in SREX. In addition to these regions, the Arctic, Antarctic, South Asia and South-East Asia) and three global analysis domains: land only, sea only and all points. Read more
Demo video
You can navigate between the various predefined regions.

2. Settings & Outputs : Modify the default settings and select the output type and values.


You can navigate between an intermediate (RCP4.5) and a high concentration (RCP8.5) pathways.

You can navigate between one control and two (near and far) future time horizons.

You can modify the layout of the chart to display individual simulations or the envelope-based on all simulations.

You can display values for various statistics such as the mean and the spatial standard deviation. The spatial correlations are computed only between historical simulations and the reference data for the present (1981-2010).

You can filter the simulations and keep only identical simulations for all climate variables such as precipitation and temperature.

3. Seasonal Cycle of monthly mean air Temperature

4. Seasonal Cycle of monthly precipitation totals

1. Select a region : Explore and navigate through various predefined regions (EURO-CORDEX, PRUDENCE, European countries)

You can navigate between the various predefined regions.

2. Settings & Outputs : Modify the default settings and select the output type and values.


You can navigate between an intermediate (RCP4.5) and a high concentration (RCP8.5) pathways.

You can navigate between one control and two (near and far) future time horizons.

You can modify the layout of the chart to display individual simulations or the envelope-based on all simulations.

You can display values for various statistics such as the mean and the spatial standard deviation. The spatial correlations are computed only between historical simulations and the reference data for the present (1981-2010).

You can filter the simulations and keep only identical simulations for all climate variables such as precipitation and temperature.

3. Seasonal Cycle of monthly mean air Temperature

4. Seasonal Cycle of monthly precipitaiton totals



Data Users | Global Climate Models

TIPS | The global climate models constitute powerful tools for climate projection to provide the best representation of the projected climate signal over a region of interest. The climate simulations evaluated here are based on the Coordinated Regional Climate Downscaling Experiment over Europe (EURO-CORDEX) to produce the best estimates of regional/local climate signal that in turn can be used in impact studies. You can click on the dashboard to navigate between other items.

1. Select a region : Explore and navigate through various regions (AR5 predefined regions)

The regions used the AR5 Reference Regions which include 26 regions defined in SREX. In addition to these regions, the Arctic, Antarctic, South Asia and South-East Asia) and three global analysis domains: land only, sea only and all points. Read more
Demo video

2. Settings & Outputs : Modify the default settings and select the output type and values.


You can navigate between an intermediate (RCP4.5) and a high concentration (RCP8.5) pathways.

You can navigate between one control and two (near and far) future time horizons.

You can modify the layout of the chart to display individual simulations or the envelope-based on all simulations.

You can filter the output to selected simulations in the meta data table or display all simulations (default).

You can display or hide the legend in the different charts

You can group the simulations by values in the meta data table, for instance, by global climate model ID, i.e. all simulations sharing the same global climate model belong to the same group but different colors are applied for simulations within each group.

You can apply the same color within grouped simulations by values in the meta data table such as the global climate model ID. In this case, all simulations within each group will have same colored lines

You can transform the values into anomalies by subtracting the mean, compute the bias or the root mean square errors as deviations with regards to the reference data, or compute the climate change with regards to the base period 1981-2010

You can display values for various statistics such as the mean and the spatial standard deviation. The spatial correlations are computed only between historical simulations and the reference data for the present (1981-2010).

You can filter the simulations and keep only identical simulations for all climate variables such as precipitation and temperature.

3. Evaluate the seasonal cycle in simulated Mean Air Temperature

4. Evaluate the seasonal cycle in Simulated Monthly Precipitation totals

5. Scatter Plots of Simulated Climate Variables



Data Users | Regional Climate Models

TIPS | The regional climate model simulations constitute a better representation of regional climate outcomes than global climate outputs as they are run on higher spatial resolution, and include more local processes to provide the best representation of the climate signal over a region of interest. The climate simulations evaluated here are based on the Coordinated Regional Climate Downscaling Experiment over Europe (EURO-CORDEX) to produce the best estimates of regional/local climate signal that in turn can be used in impact studies. You can click on the dashboard to navigate between other items.

1. Display the region (EURO-CORDEX, PRUDENCE, European countries)

2. Settings & Outputs : Modify the default settings and select the output type and values.


You can navigate between an intermediate (RCP4.5) and a high concentration (RCP8.5) pathways.

You can navigate between one control and two (near and far) future time horizons.

You can modify the layout of the chart to display individual simulations or the envelope-based on all simulations.

You can filter the output to selected simulations in the meta data table or display all simulations (default).

You can display or hide the legend in the different charts

You can group the simulations by values in the meta data table, for instance, by global climate model ID, i.e. all simulations sharing the same global climate model belong to the same group but different colors are applied for simulations within each group.

You can apply the same color within groupped simulations by values in the meta data table such as the global climate model ID. In this case, all simulations within each group will have same colored lines

You can apply the same color within groupped simulations by values in the meta data table such as the global climate model ID. In this case, all simulations within each group will have same colored lines

You can display values for various statistics such as the mean and the spatial standard deviation. The spatial correlations are computed only between historical simulations and the reference data for the present (1981-2010).

You can filter the simulations and keep only identical simulations for all climate variables such as precipitation and temperature.

3. Evaluate the seasonal cycle in simulated Mean Air Temperature

4. Evaluate the seasonal cycle in Simulated Monthly Precipitation totals

5. Scatter Plots of Simulated Climate Variables



Product Users | Changes in Climate

Here, you can
  1. select a region to navigate through various predefined regions (EURO-CORDEX, PRUDENCE, European countries)
  2. modify the default settings and select the output type (e.g. chart, boxplot) and values (e.g. bias, change)
  3. evalutate future changes in monthly mean air Temperature statistics
  4. evaluate future changes in monthly precipitaiton totals statistics

TIPS | Changes in Climate can be obtained using various climate models such as global climate models, regional climate models, and empirical statistical climate models which constitute powerful tools to provide the best representation of the projected climate signal over a region of interest. The climate simulations evaluated here are based on the CMIP5 global climate models, these are in turn used to force regional climate models (Coordinated Regional Climate Downscaling Experiment) and Empirical-Statistical Models (ESD) to produce the best estimates of regional/local climate signal that in turn can be used in impact studies. You can click on the dashboard to navigate between other items.

CMIP5 Global Climate Model Simulations


You can navigate between various AR5 predefined regions.

You can navigate between an intermediate (RCP4.5) and a high concentration (RCP8.5) pathways.

You can navigate between one control and two (near and far) future time horizons.

You can modify the layout of the chart to display individual simulations or the envelope-based on all simulations.

You can display values for various statistics such as the mean and the spatial standard deviation. The spatial correlations are computed only between historical simulations and the reference data for the present (1981-2010).

You can filter the simulations and keep only identical simulations for all climate variables such as precipitation and temperature.

EURO-CORDEX Regional Climate model Simulations


You can navigate between various PRUDENCE predefined regions (! not yet implemented).

You can navigate between an intermediate (RCP4.5) and a high concentration (RCP8.5) pathways.

You can navigate between one control and two (near and far) future time horizons.

You can modify the layout of the chart to display individual simulations or the envelope-based on all simulations.

You can display values for various statistics such as the mean and the spatial standard deviation. The spatial correlations are computed only between historical simulations and the reference data for the present (1981-2010).

You can filter the simulations and keep only identical simulations for all climate variables such as precipitation and temperature.

Figure Details

Sectoral Communication | Standardized Precipitation Index

Here, you can
  1. Modify the default settings and select the GCM/RCM simulation
  2. Evaluate statistics of the Standardized Precipitation Index (SPI) over the European domain
  3. Evaluate statistics of the Standardized Precipitation and Evaporation Index (SPEI) over the European domain

TIPS | Standardized Precipitation Index (SPI) is used as an illustration of a derived product in the sector-specific examples

Read More (e.g. Hayes et al. 1999). SPI characterizes the deficit (or abundance) of water at different temporal scales with respect to normal conditions in a reference period. SPI is flexible, easy to interpret and simple to calculate, as it uses only monthly precipitation as input. The main limitations of SPI is its incapability to take other meteorological factors which affect the water balance such as evapotranspiration into account when estimating water availability. Due to this deficiency, SPI has also been criticised for being insensitive to temperature changes. Notwithstanding these limitation, SPI is widely used in drought monitoring at different temporal scales and its use is also supported by the World Meteorological Organization (WMO). SPI is calculated from monthly precipitation time series as follows. First, a parametric (typically gamma or Pearson III -type) distribution is fitted to the monthly data. In the example data, gamma distribution has been used. To take precipitation seasonality into account, separate distributions are fitted at each month. Using the fitted parameters, cumulative probabilities are calculated for each accumulated precipitation value. In the second step, the obtained cumulative distribution values are normalised such that they follow Gaussian distribution, which enables the comparison between different locations. In other words, SPI is simply the distance from the (zero) median measured in normalised standard deviations. When raw monthly values are used, the SPI lacks “memory” in the sense that previous precipitation conditions are not taken into account in the calculation of SPI. As different hydrological aspects (soil moisture vs. runoff) respond at different time scales to the lack or surplus of water, monthly SPI is often unsuitable for drought characterization. Depending on the application, SPI is usually calculated after accumulating precipitation over certain (moving) time window (3-month, 6-month etc.). Here, SPI has been estimated using 1, 3, 6 and 12-month time window. the main criticism against using SPI in climatic studies is that it does not take changes in temperature and its effect to water availability (e.g. through evapotranspiration) directly into account when assessing drought conditions. Due to this it has been argued that SPI might not be suitable for estimating the effect of climate change to drought occurrence. To take this limitation better into account, Vicente-Serrano et al. (2010) developed an alternative index, SPEI, which does not suffer from this limitation. SPEI is estimated from water deficit (or surplus) measured as the difference between monthly precipitation and potential evapotranspiration. SPEI is not limited to any particular formulation of potential evapotranspiration and different formulas can be used based on the available input data. In the following calculations, the formulation presented by Hargreaves (1994), which is based on monthly minimum and maximum temperature, is used. In the original paper, log-logistic distribution was used to model the water deficit and the same statistical model is also used here. As in the case of SPI, the obtained cumulative probabilities are finally transformed to follow normal distribution, i.e., the interpretation of SPEI is similar to SPI.

Calculation of SPI and SPEI statistics

The calculation of both SPI and SPEI has been made with the SPEI package available for R. First, the described distributions are fitted to data in the reference period (1981-2010). The obtained distribution parameters are then used to derive SPI and SPEI values for each land grid box in the European region. The obtained time series are then used to evaluate changes in the occurrence and spatial extent of drought (as measured by SPI and SPEI) with respect to 1981-2010, assuming that future precipitation and precipitation deficit come from the same distribution. The following statistics are calculated from these time series to facilitate the visualisation of changes in SPI and SPEI for different GCM-RCMs and drought categories: nMonths = Number of months belonging to a specific drought category nEvents = Number of events belonging to a certain drought category meanEventLength = Mean length of events belonging to a certain category These statistics are calculated for each of the drought categories described above and in addition also to different combinations of categories (e.g. moderately dry + severely dry 0 extremely dry (MD+SD+ED)). The category naming follows Table 2 and the statistics have been calculated for 1-, 3-, 6- and 12-month time windows, using grid points at which the land fraction is greater than 50%.

Identified issues with SPI and SPEI

Both SPI and SPEI have been estimated using only one distribution for each (gamma for SPI and log-normal for SPEI) and might not be optimal for all aggregation scales, different times of the year and geographical locations. In addition, the unbiased probability-weighted moments used to estimate the distribution for log-normal distribution seem to lead a slight underestimation of the spread of the distribution (Beguería et al. 2014). Both of these issues can be inferred from the reference period values as differing numbers of months belonging to a specific category than what would be expect after normalisation to normal distribution. In addition, the estimation of cumulative probabilities using gamma distribution in SPI gives infinite values in arid regions (mostly in Africa) when 1-month and 3-month aggregation is used.

References

  • Hayes, M.J., Svoboda, M.D., Wilhite, D.A. and Vanyarkho, O.V., 1999. Monitoring the 1996 drought using the standardized precipitation index. Bulletin of the American meteorological society, 80(3), pp.429-438.
  • Vicente-Serrano, S.M., Beguería, S. and López-Moreno, J.I., 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of climate, 23(7), pp.1696-1718.
  • Hargreaves, G.H., 1994. Defining and using reference evapotranspiration. Journal of Irrigation and Drainage Engineering, 120(6), pp.1132-1139.
  • Beguería, S., Vicente‐Serrano, S.M., Reig, F. and Latorre, B., 2014. Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. International Journal of Climatology, 34(10), pp.3001-3023.

1. Settings & Outputs : Modify the default settings and select the output type and values.

2. Evaluate the SPI - Standardized Precipitaiton Index - over Europe

Your settings are the following:

3. Evaluate the SPEI - Standardized Precipitaiton and Evaporation Index - over Europe