Attribution in its general definition aims to quantify drivers of change in
a system. According to IPCC Working Group II (WGII) a change in a natural, human or managed
system is attributed to climate change by quantifying the difference between
the observed state of the system and a counterfactual baseline that
characterizes the system's behavior in the absence of climate change, where
“climate change refers to any long-term trend in climate, irrespective of
its cause” (IPCC, 2014). Impact attribution following this definition remains a challenge
because the counterfactual baseline, which characterizes the system
behavior in the hypothetical absence of climate change, cannot be observed.
Process-based and empirical impact models can fill this gap as they allow us to
simulate the counterfactual climate impact baseline. In those simulations,
the models are forced by observed direct (human) drivers such as land use
changes, changes in water or agricultural management but a counterfactual
climate without long-term changes. We here present ATTRICI (ATTRIbuting
Climate Impacts), an approach to construct the required counterfactual
stationary climate data from observational (factual) climate data. Our
method identifies the long-term shifts in the considered daily climate
variables that are correlated to global mean temperature change assuming a
smooth annual cycle of the associated scaling coefficients for each day of
the year. The produced counterfactual climate datasets are used as forcing
data within the impact attribution setup of the Inter-Sectoral Impact Model
Intercomparison Project (ISIMIP3a). Our method preserves the internal
variability of the observed data in the sense that factual and
counterfactual data for a given day have the same rank in their respective
statistical distributions. The associated impact model simulations allow for
quantifying the contribution of climate change to observed long-term changes
in impact indicators and for quantifying the contribution of the observed
trend in climate to the magnitude of individual impact events. Attribution
of climate impacts to anthropogenic forcing would need an additional step
separating anthropogenic climate forcing from other sources of climate
trends, which is not covered by our method.
Introduction
Global mean temperature (GMT) has recently surpassed 1 ∘C warming
above pre-industrial levels (IPCC, 2018). The impact of the realized change
in climate has also started to become detectable in natural, human or managed systems such as freshwater resources, terrestrial water systems,
coastal systems, oceans, food production systems, the economy, human health,
security and livelihoods (IPCC, 2014). The causal chain from climate change
to climate impacts is often complex and intertwined with additional drivers,
such as changes in management that alter climate-induced changes in crop
yields (Butler et al., 2018; Iizumi et al., 2018; Zhu et al., 2019) and land use changes adding to climate-driven changes in biodiversity
(Hof et al., 2018).
Attribution in its most general definition aims to quantify the drivers of
change in a system. The systems and drivers considered in attribution
studies vary between disciplines. In climate science, the “classical”
attribution framework refers to the attribution of changes in the climate
system to anthropogenic forcing (Hegerl et al., 2010; WGI contribution to
IPCC, 2013) (“climate attribution”; see first arrow in Fig. 1). It addresses
the following question: what is the contribution of anthropogenic emissions of
greenhouse gases and aerosols or land use changes to observed changes in
climatic variables, most prominently temperature and precipitation? As the
response of the climate system to these forcings is often veiled by the
chaotic nature of the climate system, climate attribution usually builds on
probabilistic approaches comparing an entire ensemble of climate model
simulations including anthropogenic forcings against a counterfactual
ensemble excluding these forcings as, e.g., generated within DAMIP (Gillett et al., 2016) to separate forced changes from internal variability. Climate
attribution can refer to observed long-term trends (WGI contribution to IPCC,
2013, chap. 10) or individual events (Trenberth et al., 2015;
NAS, 2016; Stott et al., 2016). Given the probabilistic setting, results are
often formulated as statements such as “anthropogenic climate forcing has
increased the probability of occurrence of the observed trend or the
intensity or duration of a specific extreme event”. In a non-probabilistic
framework the intensity of an observed event can be attributed to the
observed realization of climate change by comparing the event magnitude in
the observed time series to the magnitude of the same event in a detrended
version of the observed time series (quantification of the “contribution of
the observed trend to event magnitude”, Diffenbaugh et al., 2017). This
type of attribution to climate change does not address the reasons of the
observed climate trend.
Differences between drivers and affected systems in attribution research. Climate attribution (first arrow) is a focus of IPCC WGI (IPCC, 2013), and (climate) impact attribution is a focus of IPCC WGII (IPCC, 2014, chap. 18). The methodology and data presented here facilitate the use of (process-based) impact models to attribute observed changes in human, managed and natural systems to climate change (second arrow). The additional step of attribution to anthropogenic climate forcing (first and second arrow) is not addressed here.
In addition to climate attribution, research on impact attribution
addresses the following question: to what degree are observed changes in natural,
human and managed systems induced by observed changes in climate (Fig. 1,
second arrow)? In the Working Group II (WGII) contribution to the IPCC AR5, an entire chapter
was dedicated to the topic including the following definition: an impact of
climate change is “detected” if the observed state of the system differs
from a counterfactual baseline that characterizes the system's behavior in
the absence of changes in climate (IPCC, 2014, chap. 18.2.1), and
“attribution” is the quantification of the contribution of climate change to
the observed change in the natural, human or managed system. In both cases
“climate change refers to any long-term trend in climate, irrespective of
its cause” (IPCC, 2014, chap. 18.2.1).
While in principle changes in natural, human and managed systems could also
be attributed to anthropogenic climate forcing (“impact attribution to
anthropogenic climate forcing”, first and second arrow in Fig. 1, Pall et al., 2011; Schaller et al., 2016; Mitchell et al., 2016), we focus on
“impact attribution to climate change” as described in the WGII definition
and introduce a climate dataset that can be used as input to climate impact
models to characterize the system's behavior in the absence of climate
change (second arrow in Fig. 1). The dataset is derived from the observed
realization of climate, excluding the analysis how climate variability could
produce alternative realizations of factual or counterfactual climate. The
attribution approach is thus deterministic and not probabilistic, focusing
on the separation of climate change from direct human influences as
potential drivers of changes in the impacted systems. Concerning the
internal variability within impacted systems, impact models to date largely
do not resolve such variability and model a deterministic response to
external drivers. Our approach would however allow for probabilistic
attribution to climate change once impact models resolve internal
variability.
The method proposed here is designed to generate a stationary climate
without long-term changes. The statistical model used to produce this
counterfactual climate removes the long-term change correlated with (but not
necessarily caused by) large-scale climate change, represented by GMT change
instead of a simple temporal trend (see Methods). The method preserves the
internal variability of the observed time series by additively (e.g., for
temperature) or multiplicatively (e.g., for precipitation) removing a
long-term trend, such that a particularly warm or dry day compared to the
long-term trend remains a particularly warm or dry day in the counterfactual
climate. In this regard, the approach is similar to the subtraction of a
climate trend done by Diffenbaugh and Burke (2019) to attribute historical
changes in economic growth or to attribute changes in land area burned by
wildfires (Abatzoglou and Williams, 2016). However, while both studies
subtract the anthropogenic warming derived from climate model simulations,
we subtract the realized long-term trend of the data irrespective of its
cause (see WGII definition).
Attribution of impact event magnitude and trends in impact indicators to trend in climate. First, in an evaluation step it has to be demonstrated that historical impact observations (black line) can be explained by the process understanding as represented in the applied impact model and available knowledge about historical climate and socio-economic forcings. To this end, the factual simulations forced by historical climate and socio-economic forcings (solid blue line) are compared to the impact observations (solid black line). Secondly, in the attribution step the impact model is driven by counterfactual climate while all other drivers are kept equal to the factual simulation (counterfactual simulation; solid orange line). A comparison between factual and counterfactual simulations allows for the attribution of long-term changes (e.g., trends) in the impact indicator to trends in climate (contribution to trend, CT). In addition, the contribution of climate change to the magnitude of individual events (impact event attribution) can be determined as the difference between the simulated factual event magnitude and the counterfactual impact event magnitude (CE).
The stationary climate dataset can then be used as input to climate impact
models for impact attribution to climate change, as illustrated in Fig. 2.
In a first step the climate impact model forced by observed climate and
socio-economic drivers has to demonstrate being able to reproduce the
observed changes in natural, human and managed systems as measured by an
impact indicator (comparison of black and blue solid lines in Fig. 2). The
attribution of the observed changes in natural, human and managed systems is
built on a high explanatory power of the factual simulations. Then, in a
second step, that factual simulation can be compared to a counterfactual
simulation, forced by counterfactual climate but otherwise the same input as
in the factual simulation. Such a comparison allows for a quantification of
the contribution of climate change to both the observed trend in the impact
indicator (CT in Fig. 2) and the observed magnitude of an individual impact
event (CE in Fig. 2). This assumes that the climate impact model calibrated
to perform well in the factual simulation performs robustly also with
counterfactual climate input data.
Process-based impact models such as those taking part in the ISIMIP project
(http://www.isimip.org, last access: 4 August 2021) are ideal tools to address impact attribution
as they generally describe the response of natural, human or managed systems
not only to climate but also direct (human) drivers. For example, crop
models can simulate the response of crop yields to changes in land use,
irrigation patterns, fertilizer input and crop varieties (Lobell et al., 2011; Challinor et al., 2014; Minoli et al., 2019).
Similarly, hydrological models can be used to simulate how dam construction
and water withdrawal affect river discharge (Veldkamp et al., 2017, 2018). In
addition, those models allow for a process-based representation of the
extent of, e.g., river floods and droughts that can be combined with maps of
asset distribution and empirical damage functions to estimate the direct
economic damages induced by weather extremes. The impact attribution
framework could then be used to approximate the contribution of climate
change to observed trends in reported damages. Using process-based climate
impact models, this contribution can be explicitly separated from changes in
damages driven by changes in exposure or vulnerability. In this regard it goes beyond available approaches of damage attribution that simply estimate the contribution of anthropogenic climate forcing to observed damages by multiplying the fraction of attributable risk associated with weather extremes by the observed damage (Frame et al., 2020). In the same way, it could improve the
attribution of health impacts (Mitchell et al., 2016).
In this paper we introduce a detrending method tailored to support impact
attribution and illustrate its application to one of the observational
climate datasets provided within ISIMIP3a (https://protocol.isimip.org/protocol/ISIMIP3a, last access: 4 August 2021; see Sect. 2 below).
The quality of the associated impact attribution studies will critically
depend on the quality of that observational dataset. Deficits in the
observational data may lead to artifacts in the derived historical trends.
For the dataset used here, we identify some of those artifacts. Since it is
expected that other artifacts will be found for other observational
datasets, impact attribution studies should ideally be based on a range of
different observational datasets to facilitate a quantification of the
contribution of observational climate data uncertainty to the uncertainty of
the attribution results. This is also planned within ISIMIP3a. For the
dataset considered here and potential additional ones we propose a
collection of control plots that should be used to scan the observational
climate data for artifacts in preparation of each individual impact
attribution study. While we provide the control plots for a set of
large-scale world regions and all climate variables covered by our
observational climate dataset, they should be adjusted to the regions and
variables of interest in an impact attribution study as part of the analysis
of the factual impact simulation (Fig. 2). Low-quality factual climate
forcing data are expected to result in a low-quality reproduction of observed
variations in the impact indicators of interest. If that is the case, the
simulation setup outlined here does not allow for an attribution of the
observed changes in impacts to climate change.
Data
For ISIMIP3a, we construct counterfactual climate data for the global
observational dataset GSWP3-W5E5. This dataset has daily temporal and
0.5∘ spatial resolution and consists of two parts: W5E5 v2.0 for
the period 1979–2019 and GSWP3 v1.09 homogenized with W5E5 for the period
1901–1978. In the following, we describe these two parts as well as why and
how they were combined for ISIMIP3a.
The GSWP3 v1.09 dataset is from the third phase of the Global Soil Wetness
Project (GSWP3), an ongoing land model intercomparison project, which shares
its experiment protocol with “land-hist” of the Land Surface, Snow and
Soil moisture Model Intercomparison Project (LS3MIP; van den Hurk et al., 2016) and covers the years 1901–2014 (Kim, 2017). It is a dynamically
downscaled and bias-adjusted version of the 20th Century Reanalysis (20CR;
Compo et al., 2011) and has been used as a meteorological forcing dataset in
several climate impact assessments such as those carried out in ISIMIP2a
(e.g., Müller Schmied et al., 2016; Chang et al., 2017; Schewe et al., 2019; Padrón et al., 2020) as well as in broader modeling studies (e.g.,
Krinner et al., 2018; Tangdamrongsub et al., 2018; Tokuda et al., 2019). GSWP3
is also provided for the impact model evaluation task within ISIMIP3a.
20CR assimilates subdaily surface pressure and sea-level pressure
observations and uses monthly sea-surface temperature (SST) and sea-ice
distributions from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST;
Rayner et al., 2003) as lower boundary conditions. To produce GSWP3, the
first of the 56 members of the 20CR ensemble was dynamically downscaled to
T238 (about 0.5∘) spatial resolution using the incremental
correction of a single member (ICS) method of Yoshimura and Kanamitsu (2013)
and the Scripps Institution of Oceanography (SIO)/Experimental Climate
Prediction Center (ECPC) Global Spectral Model (GSM) with spectral nudging
(Yoshimura and Kanamitsu, 2008) and vertically weighted damping coefficients
(Hong and Chang, 2012). The ICS method additively adjusts the prognostic
fields of a single ensemble member such that at the monthly timescale each
adjusted field is identical to the corresponding ensemble mean field while
all higher-frequency parts of the fields are retained. Hence, the adjusted
fields represent the 20CR best estimates at the monthly timescale while
they do not suffer from the increase in synoptic variability over time found
in the 20CR ensemble mean (Compo et al., 2011) that is due to a decrease in
the ensemble spread over time, which in turn reflects the increase in
available observational constraints (Yoshimura and Kanamitsu, 2013).
The downscaled 3-hourly data were then bilinearly interpolated from T238 to
a regular 0.5∘ latitude–longitude grid. In addition, selected
variables (precipitation, surface downwelling shortwave and longwave
radiation, near-surface wind speed, near-surface air temperature, surface
air pressure, and near-surface specific humidity) were bias-adjusted with
different methods and observational reference datasets. Precipitation was
bias-adjusted at the monthly timescale using an undercatch-corrected
version of the Global Precipitation Climatology Centre (GPCC) Full Data
Monthly Product Version 7 (Schneider et al., 2014). The bias adjustment was
done by rescaling all monthly mean values to the GPCC estimates. Radiation
was bias-adjusted at the daily timescale using Surface Radiation Budget
(SRB; Stackhouse et al., 2011) primary-algorithm estimates of daily mean
values from SRB release 3.1 for longwave radiation and SRB release 3.0 for
shortwave radiation. Since those estimates are only available for 1983–2007,
bias-adjusted daily values were computed as the sum of a rescaled monthly
mean value and a rescaled daily anomaly from the monthly mean, with the
rescaling done such that, for the 1983–2007 time period, both the monthly
mean climatology and the anomaly standard-deviation climatology matched the
respective SRB estimates. Wind speed was bias-adjusted at the monthly timescale over land using mean monthly climatologies from the Climatic Research
Unit (CRU) CL2.0 dataset (New et al., 2002). The bias adjustment was done by
monthly rescaling such that the 1961–1990 mean monthly climatology matched
that of CRU CL2.0. Temperature, pressure and humidity were bias-adjusted at
the 3-hourly timescale using the WATCH forcing data methodology (Weedon et al., 2014) and monthly mean temperatures plus monthly mean diurnal
temperature ranges from the CRU TS3.23 dataset (Harris et al.,
2014), which covers the full 1901–2014 time period.
As a consequence of its derivation, the quality of the GSWP3 data varies
over time. It varies in line with variations in the availability of the
pressure, SST and sea-ice observations used to produce 20CR (Compo et al., 2011; Rayner et al., 2003) as well as with variations in the availability of
the precipitation and temperature observations used to bias-adjust GSWP3
(Schneider et al., 2014; Weedon et al., 2014). Examples of temporal
inhomogeneities in GSWP3 that are relevant for this study include artificial
drying trends over northwest China and the Tibetan Plateau over the first
half of the 20th century (Fig. 10) that are inherited from GPCC (Chen and
Frauenfeld, 2014) and spurious trends in shortwave radiation and wind speed
over Alaska, Northern Canada and Greenland over the first half of the 20th
century (Figs. 9 and S12 in the Supplement), which are related to artificial extratropical
cyclone trends in 20CR over that time period (Wang et al., 2013). Generally,
the quality of 20CR, and hence GSWP3, becomes relatively stable around
mid-century over the Northern Hemisphere, earlier over Europe and later over
the Southern Hemisphere, in line with variations in the availability of
pressure observations for data assimilation in the reanalysis (Compo et al.,
2011).
The W5E5 v2.0 dataset (Lange et al., 2021) was compiled to support the bias
adjustment of climate input data carried out within ISIMIP3b and covers the
years 1979–2019. It combines the WFDE5 v2.0 dataset (WATCH Forcing Data
methodology applied to ERA5 reanalysis data; Cucchi et al., 2020) over land
with data from the latest version of the European Reanalysis (ERA5; Hersbach
et al., 2020) over the ocean. WFDE5 is a meteorological forcing dataset based
on ERA5. For the variables included, it is a spatially aggregated (to
0.5∘) and bias-adjusted version of ERA5. Compared to 20CR used
for GSWP3, many more observations were used for data assimilation in ERA5,
including precipitation observations (Hersbach et al., 2020). That is why we
consider ERA5 to better represent reality than 20CR for 1979 onwards.
Similarly, WFDE5 is considered to better represent reality than GSWP3, in
particular with respect to day-to-day variability for variables that were
bias-adjusted using only monthly mean values in both datasets, such as
temperature and precipitation.
Since W5E5 is considered the more realistic dataset but only covers
1979–2019, it was extended backward in time to generate GSWP3-W5E5 for
ISIMIP3a. In this extended dataset, GSWP3 data for 1901–1978 were
homogenized with W5E5 data using the ISIMIP2BASD v2.5 quantile mapping
method (Lange, 2019, 2021). The resulting GSWP3-W5E5 data are identical to
the original W5E5 data from 1979 onwards but different from the original
GSWP3 data before 1979. The goal of the homogenization was to smooth the
transition from one dataset to the other in 1978/1979. To that end, for
every climate variable and grid cell individually, the original GSWP3 time
series for 1901–2004 were quantile-mapped to time series which have the
same trends but whose distributions match those of the corresponding W5E5
data over the 1979–2004 reference period. The resulting, homogenized GSWP3
data for 1901–1978 were then used to extend W5E5 backward in time. The
preservation of trends implies that differences between trends in GSWP3 and
W5E5 data were not homogenized. Consequently, some inhomogeneities at the
1978/1979 transition remain. This problem particularly affects surface
downwelling shortwave radiation over northern Europe and the Mediterranean
Basin (Fig. 8) as discussed further in the results section.
Methodology
Assuming that “climate change refers to any long-term trend in climate,
irrespective of its cause” (IPCC, 2014, chap. 18), we here present a method
to generate time series of stationary climate data from observational daily
data by removing the long-term trend while preserving the internal
day-to-day variability. In the following, we first describe the general
characteristics of our approach followed by a more detailed formal
description of the method. Then we introduce the set of global and regional
evaluation plots we recommend to regionally adjust and consider within each
attribution study using the counterfactual data generated here or when
applying the detrending approach to other observational climate data.
Detrending method
A very basic detrending approach would fit a linear temporal trend for all
data of each day of the year assuming normally distributed residuals and
remove the estimated trends from the data for each day of the year
separately. In this approach the trend estimates would not only vary
according to systematic variations in trends from one day of the year to the
other but also randomly fluctuate from one day of the year to the next one
in terms of the uncertainties associated with the individual estimates.
We go beyond this very basic approach by (i) using global mean temperature
change instead of time as a potentially powerful predictor of regional
changes in climate, (ii) allowing for non-normal distributions of the
unexplained random year-to-year fluctuations of data per day of the year,
and (iii) ensuring a smooth variation of estimated model parameters from one
day of the year to the other.
The use of GMT change, T, as the predictor of regional climate change is
motivated by the classical pattern scaling approach (Santer et al., 1990; Mitchell, 2003), with newer approaches including additional predictors
such as a distinction between land and sea to improve accuracy (Herger et al., 2015). Here, T is GMT change since 1901 smoothed by singular
spectrum analysis (SSA, Ghil et al., 2002) with a smoothing window of 10 years (Fig. 3). The smoothing of the predictor is applied because we only
want to remove long-term trends from the regional climate time series.
Natural climate variability on shorter timescales due to phenomena such as
the El Niño–Southern Oscillation is retained.
Time series of GMT change since 1901 derived from GSWP3-W5E5 near-surface air temperature data. Shown are annual mean GMT change (gray) and GMT change smoothed by SSA with a smoothing window of 10 years (pink). The smoothed GMT change is used as the predictor of regional climate change in our detrending model (denoted by T in the text).
Using T as the predictor means that we remove long-term trends in regional
climate to the extent that those are correlated with GMT change, but
irrespective of the cause of global warming. The success of the detrending
is evaluated by a number of control measures described in Sect. 3.3.
For each day of the year t the detrending is done with quantile mapping (Wood
et al., 2004; Cannon et al., 2015; Lange, 2019) from the factual
distribution AT,t to the counterfactual distribution
AT=0,t. The dependence of A on T is modeled via the
expected value μ of the distribution, using a generalized linear model
(GLM) or beta regression (Ferrari and Cribari-Neto, 2004) with a link
function g defined by gμT,t=c0t+c1tT. The link function g is used to account
for climate variables that can only be positive (in that case, gx=lnx) or can only take values between 0 and 1 (in
that case, gx=lnx/1-x). In all other cases gx=x. See Table 1 for an
overview of which distributions and link functions are used for the
different climate variables, and see Sect. 3.2 for further details. For
variables modeled by a Gaussian distribution, the variance σ2 of
A is assumed to stay constant for each day of the year; i.e., σ2
does not vary with T but only depends on t. For non-Gaussian
distributions, the variance is assumed to change with the expected value. In
that case we assume the shape of the distribution to stay constant for each
day of the year.
We use harmonics for the representation of the annual cycle, i.e., the
dependence of the coefficients c0t and c1t on the day of the year t. Specifically, we use
gμT,t=a0T+∑k=1nakTcoskωt+bkTsinkωt
to model the dependence of μ on T and t. Here, ω=2π365.25 and n=4 Fourier modes are used to model the annual cycle. The
GMT-change dependence of the Fourier coefficients ak,bk is modeled
linearly,
akT=akslopeT+akintercept;k=0,1,…,n,
and similarly for b1,b2,…,bn.
The distribution parameters that only depend on t are modeled using
lnνt=a0constant+∑k=1nakconstantcoskωt+bkconstantsinkωt,
where ω and n have the same values as in Eq. (1), and ν
represents σ for the Gaussian distribution, k for the gamma
distribution, α for the Weibull distribution and ϕ for the
beta distribution (see Table 1 and Sect. 3.2).
Climate variables covered by ISIMIP3a counterfactual climate datasets. Listed are each variable's short name and unit as well as the statistical distribution and link function used for detrending it. Also specified is the dependence of the distribution parameters on GMT change, T, and day of the year, t, as used in our GLM. The variables tasrange and tasskew are auxiliary variables used to detrend tasmin and tasmax.
VariableShort nameUnitStatistical distributionLink functionDaily mean near-surfaceair temperaturetasKGaussian with mean value μT,t and standard deviation σtgμ=μDaily near-surface temperaturerangetasrangeKGamma with mean value μT,t and shape ktgμ=lnμDaily near-surface temperatureskewnesstasskew1Gaussian with mean value μT,t and standard deviation σtgμ=μPrecipitationprkg m-2 s-1For wet or dry day: Bernoulliwith dry-day probability pT,tgp=lnp/1-pFor intensity of precipitation on wet days: gamma with meanvalue μT,t and shape ktgμ=lnμSurface downwelling shortwaveradiationrsdsW m-2Gaussian with mean value μT,t and standard deviation σtgμ=μSurface downwelling longwave radiationrldsW m-2Gaussian with mean value μT,t and standard deviation σtgμ=μSurface air pressurepsPaGaussian with mean value μT,t and standard deviation σtgμ=μNear-surface wind speedsfcwindm s-1Weibull with shape αt and scale βT,tgβ=lnβNear-surface relative humidityhurs%Beta with mean value μT,t and dispersion ϕtgμ=lnμ/1-μNear-surface specific humidityhusskg kg-1Derived from hurs, ps and tas Daily minimum near-surface air temperaturetasminKDerived from tas, tasrange and tasskew Daily maximum near-surface air temperaturetasmaxKDerived from tas, tasrange and tasskew
By limiting the number of Fourier modes to four we reduce the number of
coefficients to be estimated and ensure a smooth variation of the long-term
trend in μ over the course of the year but still capture seasonal to
sub-seasonal patterns such as monsoon season onsets. Setting n=4 in Eq. (1) leads to a total of 18 slope and intercept parameters to describe
the expected value μ in terms of T and t. Setting n=4 in Eq. (3) means
that nine parameters are used to describe the dependence of σ, k,
α and ϕ on t.
We use a Bayesian approach to estimate all of these parameters. This
requires the specification of prior distributions of the model parameters.
Similar to regularization techniques in frequentist approaches, the prior
allows us to focus the model fitting on plausible parameter values. This is
particularly important for numeric stability when the logit and logarithm
link functions are applied. We use a zero-centered Gaussian prior for all
parameters and all climate variables because we normalize the data before
parameter estimation. We use a standard deviation of 1.0 for a0intercept; a standard deviation of 1/2k-1 for akintercept, k=1,…,4; and a standard
deviation of 0.1 for akslope, k=0,…,4. Our choice
of priors for akintercept is based on the assumption
that the first mode with a period of 1 year explains the largest part of
the annual cycle and higher-order modes have decreasing influence. However
this is only a prior assumption; i.e., if the data show different patterns,
they can still be captured by our model. For akconstant we use the same priors as for akintercept.
We use the same priors for the parameters bk. We technically
implemented the model fitting by use of the pymc3 python package (Salvatier et al., 2016). Before the regression, all time series are
normalized to simplify the Bayesian model parameter estimation. To restore
the original units, the normalization is reversed after detrending.
The overall intention of our approach is to find appropriate parameter
values such that AT,t captures long-term trends in the
variables that can be removed by setting T to zero. This is important
because the counterfactual distributions are then defined by AT=0,t. As an example, the factual μT,t and the
counterfactual μT=0,t as well as the associated daily
values of one particular tas time series are shown in Fig. 4. The difference
between the expected values of distribution AT,⋅
(black line) and AT=0,⋅ (orange line) is due to a
vertical shift that is composed of a linear increase with T captured by
a0 and a change in the amplitude and phase of the annual cycle captured
by the Fourier coefficients ak and bk, k>0. The counterfactual
daily data are generated by quantile mapping; i.e., an observed value x that
corresponds to a certain quantile of the factual distribution AT,t is mapped to the counterfactual value x′ that corresponds to the same
quantile of the counterfactual distribution AT=0,t. We
illustrate this for an observed value x that corresponds to the 95th
percentile of the factual distribution in Fig. 4: we first obtain the
cumulative probability of the factual (i.e., observed) temperature (large
black dot in panel a) from the factual cumulative distribution function
(CDF; black line in panel b). We then obtain the counterfactual temperature
(large orange dot in panel a) from the counterfactual CDF (orange line in
panel b).
Illustration of detrending with quantile mapping sensitive to the annual cycle. Panel (a) shows the factual (black points) and counterfactual (orange points) daily mean near-surface air temperature data for the year 2016 of GSWP3-W5E5 for a single grid cell in the Mediterranean region at 43.25∘ N, 5.25∘ E. In panel (a), the black and orange lines show the temporal evolution of the expected value μ of the factual and the counterfactual distribution. In panel (b), the black and orange lines show the factual and counterfactual cumulative distribution function (CDF) for a single day (25 October 2016). The large points on the dashed vertical line in panel (a) highlight the factual (large black point) and counterfactual (large orange point) value on 25 October. They correspond to the 95th percentile in their respective distributions.
Model choices for each climate variableNear-surface air temperature, surface air pressure and surface
downwelling longwave radiation
We use the Gaussian distribution to model these variables as their values are far from
their physical lower bound of zero.
Daily minimum and maximum near-surface air temperature
They provide a measure of the diurnal temperature cycle in the daily resolved
dataset. We do not estimate counterfactual time series for tasmin and tasmax directly to
avoid large relative errors in the daily temperature range as pointed out by
Piani et al. (2010). Instead we construct counterfactuals for the auxiliary
variables tasrange = tasmax - tasmin and tasskew = (tas - tasmin)/tasrange that then determine the tasmin and tasmax
counterfactuals (Piani et al., 2010). We use the gamma
distribution to model tasrange since it has a lower bound at zero. The expected
value is modeled according to Eq. (1). The skewness of the diurnal near-surface temperature cycle, tasskew, is modeled by a Gaussian distribution. While
theoretically bounded, tasskew is never close to its bounds of zero and one. This
justifies the Gaussian model choice.
Precipitation
We use a mixed Bernoulli–gamma distribution
(Gudmundsson et al., 2012) for precipitation; i.e., the distribution
of wet versus dry days is described by a Bernoulli distribution with p describing
the probability of dry days, while the intensity of precipitation on wet days
is assumed to follow a gamma distribution. A day is considered dry if the
amount of precipitation is below 0.1 mm d-1. Wet days are all days where
the threshold is exceeded. We describe the gamma distribution by its
expected value and a shape parameter k. We assume that the expected value,
p, of the Bernoulli distribution and the expected value of the gamma
distribution vary with Tand t, while the shape parameter k of the gamma
distribution is assumed to only vary with t. If the probability of dry days,
pfactual, of the factual distribution AT,t is
larger than the probability of dry days, pcounterfactual, of the
counterfactual distribution AT=0,t, dry days are turned
into wet days at random with probability pfactual-pcounterfactual by
assigning them a small precipitation amount above the wet-day threshold.
This random conversion of dry days into wet days may result in physical
inconsistencies with other climate variables. These inconsistencies are
small by design since the new wet days are the least wet of all
counterfactual wet days.
Surface downwelling shortwave radiation
Physically
bound to positive numbers, the limit is only reached in the special case of
the polar night. We thus use a Gaussian distribution to model rsds. If quantile
mapping leads to negative values, we use the original value instead.
Near-surface wind speed
We use a Weibull
distribution to model surface wind speed. The distribution has a shape
parameter α and a scale parameter β, which both need to be
positive. The expected value of the Weibull distribution is given by βΓ1+1/α with the gamma function Γ. We model the scale parameter β by Eq. (1) using the natural
logarithm as the link function. We handle the shape parameter similar to the
standard deviation of the Gaussian distribution, being independent of GMT
change but varying with t.
Near-surface relative humidity
Near-surface
relative humidity hurs is positive and less than or equal to one. We assume
hurs to follow a beta distribution. Its expected value is allowed to vary with
T and t. The associated coefficients are estimated using a beta regression model
(Ferrari and Cribari-Neto, 2004) and Eq. (1) for the expected value, while the
dispersion parameter, ϕ, is assumed to only vary with t.
Near-surface specific humidity
The
counterfactual for huss is derived from counterfactual tas, ps and hurs using the
equations of Buck (1981) as described in Weedon et al. (2010).
Evaluation method
To evaluate the detrending method and the counterfactual GSWP3-W5E5 data, we
use the difference between multi-year averages of each climate variable over
the beginning of the time period (1901–1930) and multi-year averages over
the end of the time period (1990–2019) as a measure of the trend. We compare
this trend measure between the observed data and the counterfactual data,
for which it should be close to zero (Figs. 5 and 6). In addition, we
propose plotting the entire time series for regionally averaged annual (or
seasonal) mean values for both the original and the counterfactual climate
data. Here, we do so for annual regional averages over 21 world regions
(Giorgi and Francisco, 2000), see left panels of Figs. 7–10 and Supplement figures, but propose adjusting the regions and season for each attribution
study individually according to its focus. For our specific observational
dataset we add annual regional averages of the original GSWP3 data to check
if the homogenization of GSWP3 with W5E5 has introduced artificial trends in
the factual GSWP3-W5E5 data. To evaluate the performance of the detrending
method for each day of the year we propose to compare the 1990–2019 regional
mean climatology of the counterfactual data to the 1901–1930 regional mean
climatology of the factual data for each region of interest (right panels of
Figs. 7–10 and Supplement figures).
Results
The counterfactual dataset evaluated in the following is free to download
through the ISIMIP data portal
(https://data.isimip.org/search/climate_scenario/counterclim/, last access: 4 August 2021) along with the underlying original data. Our method
strongly reduces the observed difference between multi-year averages over
the beginning of the century (1901–1930) and the end of the observational
period (1990–2019) for most locations and variables (Figs. 5 and 6). The
remaining differences are largest for precipitation over the Tibet region
and for wind speed over Greenland. In the following we exemplarily zoom into
these regions to resolve the temporal evolution of the regionally averaged
factual and counterfactual data (Figs. 7–10, left panels) and evaluate the
detrending for each day of the year (Figs. 7–10, right panels). We start
with temperature and precipitation in northern Europe where the detrending
works well and then focus on regions where the factual data show artifacts
that may make them inadequate for impact attribution within the proposed
setup.
Differences between multi-year averages over the late (1990–2019) and early (1901–1930) time period for the factual (left) and counterfactual (right) GSWP3-W5E5 dataset. Results are shown for tas, tasmin, tasmax, pr and rsds (from top to bottom). Rectangles show the 21 world regions from Giorgi and Francisco (2000). Note that the color scale is capped for precipitation; i.e., values below -2 mm d-1 and above 2 mm d-1 are displayed in dark blue and dark red, respectively.
Same as Fig. 5 but for rlds, ps, sfcwind, hurs and huss. Note that the color scale is capped for wind at -0.5 and 0.5 m s-1 and for hurs at -12 % and 12 %. Values below and above those bounds are displayed in dark blue and dark red, respectively.
Panels (a) and (c) show annual regional mean time series of factual GSWP3-W5E5 data (solid black line), factual GWSP3 data (dashed black line) and counterfactual GSWP3-W5E5 data (orange line) for near-surface air temperature (a) and precipitation (c) over northern Europe (NEU). Panels (b) and (d) show multi-year regional mean climatologies for near-surface air temperature (b) and precipitation (d) of factual and counterfactual GSWP3-W5E5 data for NEU. To obtain the counterfactual annual cycle (orange line), our method aims to map the late factual (thin black line) to the early factual (thick black line) annual cycle.
Temperature, northern Europe (NEU)
There is essentially no
difference between the GSWP3 data and the GSWP3-W5E5 data in the period
1979–2014 where the original GSWP3 and W5E5 data overlap. Our approach
successfully removes the long-term trend from the observed time series of
regionally averaged annual temperature data (Fig. 7a) and for each day of
the year (Fig. 7b). By construction, the detrending retains the
year-to-year variability; i.e., hot days stay hot and cold days stay cold.
The counterfactual 1990–2019 averages for individual days of the year match
the seasonal evolution of the factual data at the beginning of the century
(1901–1930) as intended. In northern Europe, temperatures for each day of
the year have changed relatively uniformly throughout the year (Fig. 7b).
Precipitation, northern Europe (NEU)
The GSWP3 data are offset to
slightly higher values of precipitation compared to the GSWP3-W5E5 data in
the period 1979–2014 where the original GSWP3 and W5E5 data overlap. The
homogenization method of the GSWP3-W5E5 data transfers this offset to the
period 1901–1979, leading to a more consistent dataset. Our approach
successfully removes the long-term trend from the observed annual regional
average time series (Fig. 7c). There is a seasonality in the long-term
trend with almost no change in April and August in contrast to positive
trends in the other months (compare thick to thin black line in Fig. 7d).
Our approach successfully captures this seasonal variation of the trend. The
annual cycle of the counterfactual data in the period 1990–2019 (orange
line) matches the annual cycle of the factual data in the beginning of the
century 1901–1930 (thick black line).
Same as Fig. 7 but for shortwave radiation over the Mediterranean Basin (MED).
Shortwave radiation, Mediterranean Basin (MED)
There is a
considerable offset between the GSWP3 and W5E5 data in the overlapping
1979–2014 period (see difference between dashed and solid black line in Fig. 8). In addition, the GSWP3 data do not show a trend over the entire time
period 1901–2014, whereas there is a positive trend in the 1979–2019 W5E5
data. The harmonization has shifted the original GSWP3 data but did not
introduce a trend by design of the quantile mapping method used for it (see
Sect. 2). This results in inhomogeneous decadal trends in the GSWP3-W5E5
data and a jump at the 1978/1979 transition. This change in the
characteristics of the shortwave radiation in GSWP3-W5E5 is an artifact
introduced by the different characteristics of the GSPW3 and W5E5 data and
not related to GMT change. Thus, in this region the trend in the factual
rsds time series is not reliable enough to derive a meaningful no-climate-change counterfactual rsds time series. Annual shortwave radiation over
northern Europe is affected in a similar way (Fig. S21).
Same as Fig. 7 but for wind speed over Greenland (GRL).
Wind speed, Greenland (GRL)
The factual datasets show spurious
trends in wind speed over Alaska, northern Canada and Greenland over the
first half of the 20th century (regions GRE and ALA, Figs. 9 and S16), which are related to
artificial extratropical cyclone trends in the 20CR reanalysis over that
time period (Wang et al., 2013). Shortwave radiation in those regions is
affected in a similar way (Figs. S15 and S17). Our detrending method is
unable to distinguish spurious trends from real trends. It finds a
correlation between GMT change and the spurious trends and produces
counterfactual data that have a spurious positive trend over the second half
of the 20th century (Fig. 9a). Such counterfactual time series are clearly
not reliable.
Same as Fig. 7 but for precipitation over the Tibetan
Plateau (TIB).
Precipitation, Tibetan Plateau (TIB)
Over the first half of the
20th century the GSWP3-W5E5 precipitation data show a strong drying trend
over the TIB region that is assumed to be artificial and inherited from the
underlying GPCC dataset (Sect. 2). Since the trend is not related to global
warming, it is not well captured by our detrending model. Consequently, the
average counterfactual precipitation at the end of the observational period
does not match the average factual data at the beginning of the period
(Figs. 5h and 10b). The detrending leads to a positive trend over the
second half of the century, while the factual data do not show such a trend.
Since the observational data for the first half of the century are
considered unreliable, they are also not fit to derive a meaningful no-climate-change counterfactual.
We present further plots covering all variables and Giorgi regions in the
Supplement. Given potential artifacts in the factual data, the associated
plots have to be analyzed when planning a regional attribution study.
Discussion
The attribution of changes in the climate system to anthropogenic
interference with the climate system is a mature research field (IPCC, 2013;
Gillett et al., 2016; NAS, 2016; Stott et al., 2016). Less work has been done
on the attribution of changes in natural, human and managed systems
affected by climate change in combination with other time-evolving drivers.
Impact attribution as defined in the introduction aims to quantify the role
of climate change versus the other drivers of change. Impact attribution
needs a comparison of the observed state of the considered system to its
hypothetical, counterfactual state without climate change. The reason for
the change in climate trends and a separation of anthropogenically forced
changes from climate variability are not necessarily required. Thus, a
simplified methodology that detrends observational data is sufficient
without the need for probabilistic climate model simulations. The proposed
design of the counterfactual climate forcing data and the associated impact
simulation framework mean a restriction to “impact attribution to climate
change” instead of “impact attribution to anthropogenic climate forcing”.
The latter is necessary to, for example, attribute a fraction of an impact
to a greenhouse gas emitter and support climate litigation (Marjanac et al., 2017; Burger et al., 2020). Thus the
counterfactual climate data generated here are not intended to replace
climate simulations with counterfactual greenhouse gas forcings such as the
histNAT CMIP6 experiments' (Gillett et al., 2016) large climate model
ensembles that are required to attribute changes in climate or impacts to
anthropogenic emissions.
Climate impact models can be considered as ideal tools to address impact
attribution as they are usually designed to represent the response of impact
indicators to climate disturbances but also account for direct human
interventions such as agricultural management changes, water abstraction or
flood protection measures. Within the model, individual drivers can be
controlled, and a factual run (observed climate change + observed direct
human interventions, Fig. 2 blue line) can be compared to a counterfactual
run (counterfactual climate + observed direct human interventions, Fig. 2
orange line).
By providing climate forcing data for counterfactual climate impact runs, we
facilitate impact attribution following the basic IPCC AR5 WGII definition
utilizing the strength of impact models to address the important question of to
what degree climate change is already affecting natural, human and managed
systems. So far the contribution of climate change to long-term historical
changes in human, natural or managed systems is often addressed by model
simulation where direct human interventions are fixed while only climate is
allowed to change according to historical observations (e.g., Sauer et al., 2021). However, this alternative definition may also lead to different
results and does not allow for the attribution of the magnitude of
individual impact events to climate change as described in Fig. 2.
Attribution draws a causal connection and quantifies the change due to the
cause. An important part of the attribution work is thus to ensure that the
cause–effect relationship is correctly captured in the model. This requires
careful analysis and model evaluation to show that the change estimated by
an impact model is a reliable estimate of the real-world change. Simulated
changes need to agree with observed changes, and it needs to be ruled out
whether this agreement is due to confounding factors that drive observed
changes but are not part of the model simulations. The ISIMIP3a historical
simulations serve to address these points and demonstrate the explanatory
power of impact models as an integral part of the attribution work.
Our method ultimately builds on the correlation between a regional climate
variable and decadal GMT change to remove long-term trends in the regional
climate variables without implying causality. It is well possible that
changes in regional climate variables have other reasons than global warming
such as local effects of land use changes and aerosol emissions as well as
regional characteristics of large-scale decadal climate oscillations.
However, our study shows that GMT change is generally a powerful predictor
allowing for generating stationary counterfactual climate data. Major
detrending failures seem to be related to artifacts in the factual
observational climate data that particularly affect the first half of the
century and prevent impact attribution in the proposed framework.
Our detrending approach does not guarantee the maintenance of physical
consistency of different climate variables in the counterfactual datasets in
terms of, e.g., energy closure or water budgets. However, the applied
quantile mapping preserves ranks, which means that relatively high values
before the mapping are also relatively high after the mapping and similarly
for relatively low values. Statistically speaking, univariate quantile
mapping independently applied to all climate variables preserves the
multivariate rank distribution (the copula) over all variables. In that
sense the statistical dependence between variables is preserved by our
detrending method, and the risk of producing physically inconsistent
counterfactual climate data is at least limited. This is critical for the
attribution of the extreme event magnitude to observed climate trends (see
introduction) because several climate variables can contribute to impact
extremes.
Here, we deliberately excluded the question of what drives climate change,
i.e., the attribution of changes in the climate system to greenhouse gas
emissions, as it often implies a focus on this aspect and less attention is
paid to the separation of climate change from direct human interventions as
drivers of observed changes in natural, human and managed systems. The
restriction of the research question to “impact attribution to climate
change in general” makes the assessments independent of climate simulations
and their potential limitation in reproducing processes relevant for
historical climate change. Instead, the restricted framework is directly
linked to impact model evaluation and the question of how well we understand
the observed changes in human, natural and managed systems. This question
can most directly be addressed by the factual impact simulations proposed
here rather than with impact simulations based on simulated historical
climate. In addition, as opposed to large ensembles of climate model
simulations, such a dataset is easily integrated into an impact model
intercomparison project such as ISIMIP, which includes models of very
different computational costs. In this way the approach allows for an
exploration of structural uncertainty in climate impact attribution, based
on a multi-impact-model ensemble, combined with a variety of damage
functions where appropriate.
With the methods and data presented here, we aim to advance the field of
impact attribution and reveal past and present societal and environmental
sensitivities to climate change. Getting a better understanding of the
drivers of observed changes in natural, human and managed systems will help
us to better estimate future risks related to ongoing global warming and
develop adequate adaptation measures.
Code and data availability
The source code underlying the analysis presented in the paper (v1.1.0) is
archived at 10.5281/zenodo.5032065 (Mengel et al., 2021a). The source code to
produce the figures as appearing in the paper (v1.1.0) is archived at
10.5281/zenodo.5036701 (Mengel and Treu, 2021). All code is open to use under the
GPL license. The presented counterfactual climate dataset is archived at
10.5281/zenodo.5036364 (Mengel et al., 2021b) and based on v1.1.0 of the source
code.
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-14-5269-2021-supplement.
Author contributions
MM, ST, SL and KF developed the concept. ST and MM implemented the
methods, wrote the code and produced the data. All authors wrote the paper.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank Hyungjun Kim for
helping us to explain the making of the GSWP3 dataset. We thank Anne
Gädeke and Christoph Menz for beta testing our counterfactual data. We
are grateful to Benjamin Schmidt's early contributions to the code. We thank
the two anonymous reviewers for their helpful comments on the initial
version of this paper.
Financial support
This research has been supported by the German Federal Ministry of Education and Research (BMBF, grant no. 01LS1711A). Stefan Lange received funding from the European Union H2020 SC5-01-2014 (CRESCENDO, grant no. 641816). Simon Treu received
funding from the European Union H2020 LC-CLA-03-2018 (RECEIPT, grant no. 820712).
The publication of this article was funded by the Open Access Fund of the Leibniz Association.
Review statement
This paper was edited by Juan Antonio Añel and reviewed by two anonymous referees.
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