ICARIA: spatially distributed climate projections from statistical downscaling

ICARIA project had as one of its main purposes to develop coherent, reliable and usable downscaled climate projections from the last CMIP6 in order to construct the basis for efficient support to climate adaptation and decision-making of the related stakeholders, supporting the adaptation of critical assets within the project. These projections were obtained with also the purpose of being freely available for further use in subsequent studies and, hence, foster adaptation to climate change in more areas. Therefore, ICARIA’s climate information is already based on CMIP6 models and incorporating in its workflow the current SSPs. The presented high-resolution future climate projections display a unique dataset, being obtained from a high-quality and high-density set of weather observations that are then interpolated to the case studies of interest in a 100x100m resolution grid, which is the main outcome offered in this publication. These models will provide the scenarios to be considered within the Risk Assessment and the design and development of all adaptation measures coming as ICARIA outcomes.

For further details, find here a brief of the methodology followed:         

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The statistical downscaling methodology applied in ICARIA by FIC, named FICLIMA (Ribalaygua et al. 2013), consists of a two-step analogue/regression statistical method which has been used in national and international projects with good verification results (i.e.: Monjo et al. 2016). The first step is common for all simulated climate variables and it is based on an analogue stratification (Zorita et al. 1993). An analogue method was applied based on the hypothesis that ‘analogue’ atmospheric patterns (predictors) should cause analogue local effects (predictands), which means that the number of days that were most similar to the day to be downscaled was selected. The similarity between any two days was measured according to three nested synoptic windows (with different weights) and four large-scale fields using a pseudo-Euclidean distance between the large-scale fields used as predictors. For each predictor, the weighted Euclidean distance was calculated and standardised by substituting it with the closest percentile of a reference population of weighted Euclidean distances for that predictor. This method is a good method for reproducing nonlinear relationships between predictors and the predictands, but it could not be used to simulate values outside of the range of observed values. In order to overcome this problem and obtain a better simulation, a second step was required.

For this second step, the procedures applied depend on the variable of interest. To determine the temperature, multiple linear regression analysis for the selected number of most analogous days was performed for each station and for each problem day. From a group of potential predictors, the linear regression selected those with the highest correlation, using a forward and backward stepwise approach.

For precipitation, a group of m problem days (we use the whole days of a month) is downscaled. For each problem day we obtain a “preliminary precipitation amount” averaging the rain amount of its n most analogous days, so we can sort the m problem days from the highest to the lowest “preliminary precipitation amount”. For assigning the final precipitation amount, all amounts of the m×n analogous days are sorted and clustered in m groups. Every quantity is finally assigned, orderly, to the m days previously sorted by the “preliminary precipitation amount”.

For wind or relative humidity, the second step is a transfer function between the observed probability distribution and the simulated one using the averaged values from the n = 30 analogous days. Particularly, a parametric bias correction was performed to the time series obtained from the analogue stratification (first step). In order to estimate the improvement of this procedure, the bias correction was also applied to the direct model outputs.

This second step done at a daily scale with an inner thorough verification procedure is essential and the main differentiating process of FICLIMA method. It extends beyond mean values to include extremes and covers all time scales, including daily intervals. With the verification it can be proven If the method correctly simulates changes from one day to the next, indicating an effective capture of the underlying physical connections between predictors and predictands. These physical links remain relatively consistent, even in the face of climate change (as opposed to purely empirical relationships that might shift). In essence, this approach theoretically addresses the primary challenge in statistical downscaling known as the non-stationarity problem. This problem questions the stability of predictor/predictand relationships established in the past, probing whether these relationships will persist in the future.

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The dataset shared here includes information for the three case studies tackled in ICARIA: Barcelona Metropolitan Area (AMB), Salzburg Region (SLZ), and South Aegean Region (SAR). The information provided covers data and outcomes by 10 models belonging to CMIP6. Each model has a historical archive, from 01/01/1950 to 31/12/2014 and 4 future scenarios (ssp126, ssp245, ssp370 and ssp585) ranging from 01/01/2015 to 31/12/2100. The relation of the selected models is detailed in the next Table:

Table 1. Information about the 10 climate models belonging to the 6 Coupled Model Intercomparison Project (CMIP6) corresponding to the IPCC AR6. Models were retrieved from the Earth System Grid Federation (ESGF) portal in support of the Program for Climate Model Diagnosis and Intercomparison (PCMDI).

CMIP6 MODELS

Resolution

Responsible Centre

References

ACCESS-CM2

1,875º x 1,250º

Australian Community Climate and Earth System Simulator (ACCESS), Australia

Bi, D. et al (2020)

BCC-CSM2-MR

1,125º x 1,121º

Beijing Climate Center (BCC), China Meteorological Administration, China.

Wu T. et al. (2019)

CanESM5

2,812º x 2,790º

Canadian Centre for Climate Modeling and Analysis (CC-CMA), Canadá.

Swart, N.C. et al. (2019)

CMCC-ESM2

1,000º x 1,000º

Centro Mediterraneo sui Cambiamenti Climatici (CMCC).

Cherchi et al, 2018

CNRM-ESM2-1

1,406º x 1,401º

CNRM (Centre National de Recherches Meteorologiques), Meteo-France, Francia.

Seferian, R. (2019)

EC-EARTH3

0,703º x 0,702º

EC-EARTH Consortium

EC-Earth Consortium. (2019)

MPI-ESM1-2-HR

0,938º x 0,935º

Max-Planck Institute for Meteorology (MPI-M), Germany.

Müller et al., (2018)

MRI-ESM2-0

1,125º x 1,121º

Meteorological Research Institute (MRI), Japan.

Yukimoto, S. et al. (2019)

NorESM2-MM

1,250º x 0,942º

Norwegian Climate Centre (NCC), Norway.

Bentsen, M. et al. (2019)

UKESM1-0-LL

1,875º x 1,250º

UK Met Office, Hadley Centre, United Kingdom

Good, P. et al. (2019)

The climate projections have been developed over each of the observational locations that were retrieved to run the statistical downscaling. The results from these projections have been spatially interpolated into a 100x100m grid with a Multi-lineal Regression Model considering diverse adjustments and topographic corrections. The results presented here are the median of the 10 models used, obtained for each of the 4 SSPs and each of the time periods considered in ICARIA until the year 2100. The variables treated belong to the main climate variables and their related extreme indicators as they were defined during the ICARIA project. You can find here a summary table of all the variables and indicators that were used to develop the projections.
Table 2. Summary of selected thermal and precipitation indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events.

Index/name

Short description

Source

Variable

Units

Threshold

Thermal indicators

TX90 / TX10

Warm/cold days

Zhang et al. (2011)

TX

nd

90 / 10%

HD

Heat day

ICARIA

TX

nd

> 30 °C

EHD

Extreme heat day

ICARIA

TX

nd

> 35 °C

TR

Tropical nights

Zhang et al. (2011)

TN

nd

> 20 °C

EQ

Equatorial nights

AEMet 2020, ICARIA

TN

nd

> 25 °C

IN

Infernal nights

ICARIA

TN

nd

> 30 °C

FD

Frost days

Zhang et al. (2011)

TN

nd

< 0 °C

Max consec

Max spell length for above thermal indicators

ICARIA

-

nd

-

Nº events

Number of above thermal indicators events

ICARIA

-

ne

> 3 days

TXm

Mean maximum temperatures

ICARIA

TX

°C

-

TNm

Mean minimum temperatures

ICARIA

TN

°C

-

TM

Mean temperatures

ICARIA

TA

°C

-

HWle

Heatwave length

ICARIA

TX

nd

3d > 95% TX

HWim/HWix

Mean and maximum heatwave intensity

ICARIA

TX

°C

3d > 95% TX

HWf

Heatwave frequency

ICARIA

TX

ne

3d > 95% TX

HWd

Heatwave days

ICARIA

TX

nd

3d > 95% TX

HI - P90

Heat Index (percentile 90)

NWS (1994)

TX, RH

°C

TX>27 °C, HR> 40%

UTCI

Universal Thermal Climate Index

Bröde et al. (2012)

TARH, W

-

-

UHI

Isla de calor (BCN) anual y estacional

AMB, Metrobs 2015

T

°C

TM1-TM2 > 0 °C

Precipitation indicators

R20

Number of heavy precipitation days

Zhang et al. (2011)

P

nd

>20 mm

R50, R100

Days with extreme heavy rain

AMB et al. (2017)

P

nd

>50mm

>100mm

Ra

Yearly and seasonal rainfall relative change

ICARIA

P

mm

≥ 0.1mm

IDF - CCF

IDF Curves - Climate Change Factor

Arnbjerg-Nielsen (2012)

P

-

≥ 0.1mm

Forest fire indicators

Mean FWI

Mean Canadian FWI in fire season

Stock, B.J. et al. (1989)

RHn, TX, P, W

.

June-September

Very High FWI

Very High Canadian FWI

Stock, B.J. et al. (1989)

RHn, TX, P, W

nd

FWI > 38

Table 3. Summary of selected drought, oceanic and wind indicators, grouped aligned with the main hazards they feed. “nd” = number of days; “ne” = number of events.

Index/name

Short description

Source

Variable

Units

Threshold

Drought indicators

CDDx

Maximum dry spell duration

Zhang et al. (2011)

P

nd

< 1 mm

CDDm

Mean dry spell duration

Zhang et al. (2011)

P

nd

< 1 mm

SPI

SPI 

of 1, 3, 6, 12, 24 & 36 months

McKee et al. (1993) 

P, TA

mm

≥ 0.1mm

SPEI

SPEI 

of 1, 3, 6, 12, 24 & 36 months

Vicente-Serrano et al.  (2010)

P, TA

mm

≥ 0.1mm

Oceanic indicators

SS

Storm surge

Bryant et al. (2016)

MT

cm

-

OW

Significant/maximum wave height

ICARIA

WH

m

-

Wind indicators

EWG

Extreme wind gusts

ICARIA

W

km/h

-